Artículos de revistas: "Scanner laser dynamique (MLS)"

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Oude Elberink, S. J. "SMART FUSION OF MOBILE LASER SCANNER DATA WITH LARGE SCALE TOPOGRAPHIC MAPS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (3 de agosto de 2020): 251–58. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-251-2020.

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Abstract. The classification of Mobile Laser Scanner (MLS) data is challenging due to the combination of high variation in point density with a high variation of object appearances. The way how objects appear in the MLS data highly depends on the speed and orientation of the mobile mapping platform and the occlusion by other vehicles. There have been many approaches dealing with the geometric and contextual appearance of MLS points, voxels and segments to classify the MLS data. We present a completely different strategy by fusing the MLS data with a large scale topographic map. Underlying assumption is that the map delivers a clear hint on what to expect in the MLS data, at its approximate location. The approach presented here first fuses polygon objects, such as road, water, terrain and buildings, with ground and non-ground MLS points. Non-ground MLS points above roads and terrain are further classified by segmenting and matching the laser points to corresponding map point objects. The segmentation parameters depend on the class of the map points. We show that the fusion process is capable of classifying MLS data and detecting changes between the map and MLS data. The segmentation algorithm is not perfect, at some occasions not all the MLS points are correctly assigned to the corresponding map object. However, it is without doubt that the proposed map fusion delivers a very rich labelled point cloud automatically, which in future work can be used as training data in deep learning approaches.

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Gonzalez-Barbosa, Jose-Joel, Karen Lizbeth Flores-Rodrıguez, Francisco Javier Ornelas-Rodrıguez, Felipe Trujillo-Romero, Erick Alejandro Gonzalez-Barbosa y Juan B. Hurtado-Ramos. "Using mobile laser scanner and imagery for urban management applications". IAES International Journal of Robotics and Automation (IJRA) 11, n.º 2 (1 de junio de 2022): 89. http://dx.doi.org/10.11591/ijra.v11i2.pp89-110.

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Despite autonomous navigation is one of the most proliferate applications of three-dimensional (3D) point clouds and imagery both techniques can potentially have many other applications. This work explores urban digitization tools applied to 3D geometry to perform urban tasks. We focus exclusively on compiling scientific research that merges mobile laser scanning (MLS) and imagery from vision systems. The major contribution of this review is to show the evolution of MLS combined with imagery in urban applications. We review systems used by public and private organizations to handle urban tasks such as historic preservation, roadside assistance, road infrastructure inventory, and public space study. The work pinpoints the potential and accuracy of data acquisition systems to handled both 3D point clouds and imagery data. We highlight potential future work regarding the detection of urban environment elements and to solve urban problems. This article concludes by discussing the major constraints and struggles of current systems that use MLS combined with imagery to perform urban tasks and to solve urban tasks.

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Ahokas, E., H. Kaartinen, A. Kukko y P. Litkey. "Test field for airborne laser scanning in Finland". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (7 de noviembre de 2014): 9–12. http://dx.doi.org/10.5194/isprsarchives-xl-1-9-2014.

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Airborne laser scanning (ALS) is a widely spread operational measurement tool for obtaining 3D coordinates of the ground surface. There is a need for calibrating the ALS system and a test field for ALS was established at the end of 2013. The test field is situated in the city of Lahti, about 100 km to the north of Helsinki. The size of the area is approximately 3.5 km × 3.2 km. Reference data was collected with a mobile laser scanning (MLS) system assembled on a car roof. Some streets were measured both ways and most of them in one driving direction only. The MLS system of the Finnish Geodetic Institute (FGI) consists of a navigation system (NovAtel SPAN GNSS-IMU) and a laser scanner (FARO Focus3D 120). In addition to the MLS measurements more than 800 reference points were measured using a Trimble R8 VRS-GNSS system. Reference points are along the streets, on parking lots, and white pedestrian crossing line corners which can be used as reference targets. The National Land Survey of Finland has already used this test field this spring for calibrating their Leica ALS-70 scanner. Especially it was easier to determine the encoder scale factor parameter using this test field. Accuracy analysis of the MLS points showed that the point height RMSE is 2.8 cm and standard deviation is 2.6 cm. Our purpose is to measure both more MLS data and more reference points in the test field area to get a better spatial coverage. Calibration flight heights are planned to be 1000 m and 2500 m above ground level. A cross pattern, southwest–northeast and northwest–southeast, will be flown both in opposite directions.

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Hartley, Robin J. L., Sadeepa Jayathunga, Peter D. Massam, Dilshan De Silva, Honey Jane Estarija, Sam J. Davidson, Adedamola Wuraola y Grant D. Pearse. "Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping". Remote Sensing 14, n.º 14 (11 de julio de 2022): 3344. http://dx.doi.org/10.3390/rs14143344.

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Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R2 = 0.99, RMSE = 5.4%) and stem volume (R2 = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R2 = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement.

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Rahmadiansyah, Megan y Muhammad Iqbal Taftazani. "Pemanfaatan Data Pengukuran Mobile Laser Scanner untuk Analisis Perubahan Elevasi Ruas Tol". Journal of Geospatial Science and Technology 2, n.º 1 (29 de julio de 2024): 12–18. http://dx.doi.org/10.22146/jgst.v2i1.6097.

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Pembangunan jalan tol di Indonesia yang semakin pesat di Indonesia perlu diimbangi monitoring yang baik. Salah satu metode yang dapat dimanfaatkan untuk monitoring jalan tol adalah metode Mobile Laser Scanner (MLS) yang cukup efisien, salah satunya untuk monitoring elevasi jalan tol. Penelitian ini menggunakan data MLS Ruas Tol Terbanggi Besar Pematang Panggang Kayu Agung (TBPPKA) STA 27+500 s.d. STA 30+212 yang diambil pada tahun 2020 dan 2021 yang diolah menggunakan perangkat lunak Global Mapper dengan metode subtract surface untuk mengetahui nilai perubahan elevasinya. Hasil dari penelitian ini ditemukan adanya perubahan elevasi ruas tol TBPPKA dari tahun 2020 ke 2021 di Track A sebesar -0,017 m s.d. 0,022 m dan di Track B sebesar -0,025 m s.d. 0,019 m. The rapid development of toll roads in Indonesia must be balanced with good monitoring. One method that can be used for toll road monitoring is the Mobile Laser Scanner (MLS) method, which is quite efficient for monitoring toll road elevation. This study uses MLS data for the Terbanggi Besar Pematang Panggang Kayu Agung Toll Road (TBPPKA) STA 27+500 to STA 30+212 taken in 2020 and 2021, which is processed using Global Mapper software with the subtract surface method to determine the value of elevation changes. The results of this study found that there was a change in the elevation of the TBPPKA toll road from 2020 to 2021 on Track A of -0.017 m to 0.022 m and on Track B of -0.025 m to 0.019 m.

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Kaasalainen, S., H. Kaartinen, A. Kukko, K. Anttila y A. Krooks. "Brief communication "Application of mobile laser scanning in snow cover profiling"". Cryosphere Discussions 4, n.º 4 (30 de noviembre de 2010): 2513–22. http://dx.doi.org/10.5194/tcd-4-2513-2010.

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Abstract. We present a snowmobile based mobile mapping system and its first application on snow cover roughness and change detection measurement. The ROAMER mobile mapping system, constructed at the Finnish Geodetic Institute, consists of the positioning and navigating systems, a terrestrial laser scanner, and the carrying platform (a snowmobile sledge in this application). We demonstrate the applicability of the instrument in snow cover roughness profiling and change detection by presenting preliminary results from a mobile laser scanning (MLS) campaign. The results show the potential of MLS for fast and efficient snow profiling from large areas in a millimetre scale.

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Kaasalainen, S., H. Kaartinen, A. Kukko, K. Anttila y A. Krooks. "Brief communication "Application of mobile laser scanning in snow cover profiling"". Cryosphere 5, n.º 1 (1 de marzo de 2011): 135–38. http://dx.doi.org/10.5194/tc-5-135-2011.

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Abstract. We present a snowmobile-based mobile mapping system and its first application to snow cover roughness and change detection measurement. The ROAMER mobile mapping system, constructed at the Finnish Geodetic Institute, consists of the positioning and navigating systems, a terrestrial laser scanner, and the carrying platform (a snowmobile sledge in this application). We demonstrate the applicability of the instrument to snow cover roughness profiling and change detection by presenting preliminary results from a mobile laser scanning (MLS) campaign. The results show the potential of MLS for fast and efficient snow profiling from large areas in a millimetre scale.

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Yamamoto, K., T. Chen y N. Yabuki. "A CALIBRATION METHOD OF TWO MOBILE LASER SCANNING SYSTEM UNITS FOR RAILWAY MEASUREMENT". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (6 de agosto de 2020): 277–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-277-2020.

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Abstract. This paper proposes a methodology to calibrate the laser scanner of a Mobile Laser Scanning System (MLS) with the trajectory of the other MLS, both of which are installed directly above the top of both rails. Railway vehicle laser scanners systems of MLS are able to obtain 3D scanning map of the rail environment. In order to adapt the actual site condition of the maintenance works, we propose a calibration method with non-linear Least Mean Square calculation which use point clouds around poles along rails and sleepers of rails as cylindrical and planner constraints. The accuracy of 0.006 m between two laser point clouds can be achieved with this method. With the common planar and cylinder condition Leven-Marquardt method has been applied for this method. This method can execute without a good initial value for the extrinsic parameter and can shorten the processing time compared with the linear type of Least Mean Square method.

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Staats, B. R., A. A. Diakité, R. L. Voûte y S. Zlatanova. "AUTOMATIC GENERATION OF INDOOR NAVIGABLE SPACE USING A POINT CLOUD AND ITS SCANNER TRAJECTORY". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (14 de septiembre de 2017): 393–400. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-393-2017.

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Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user’s preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.

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Mitka, Bartosz, Przemysław Klapa y Pelagia Gawronek. "Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner". Sensors 24, n.º 18 (17 de septiembre de 2024): 6010. http://dx.doi.org/10.3390/s24186010.

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The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

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Nguyen, Hoang Long, David Belton y Petra Helmholz. "SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (9 de junio de 2016): 351–58. http://dx.doi.org/10.5194/isprs-archives-xli-b3-351-2016.

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The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

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Nguyen, Hoang Long, David Belton y Petra Helmholz. "SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (9 de junio de 2016): 351–58. http://dx.doi.org/10.5194/isprsarchives-xli-b3-351-2016.

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The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

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Oh, Sangmin, Dongmin Lee, Minju Kim, Taehoon Kim y Hunhee Cho. "Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing". Remote Sensing 13, n.º 2 (6 de enero de 2021): 161. http://dx.doi.org/10.3390/rs13020161.

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With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

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Faitli, Tamás, Eric Hyyppä, Heikki Hyyti, Teemu Hakala, Harri Kaartinen, Antero Kukko, Jesse Muhojoki y Juha Hyyppä. "Integration of a Mobile Laser Scanning System with a Forest Harvester for Accurate Localization and Tree Stem Measurements". Remote Sensing 16, n.º 17 (4 de septiembre de 2024): 3292. http://dx.doi.org/10.3390/rs16173292.

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Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH>20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used.

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Lueangvilai, Ekarin y Taweep Chaisomphob. "Application of Mobile Mapping System to a Cable-Stayed Bridge in Thailand". Sensors 22, n.º 24 (8 de diciembre de 2022): 9625. http://dx.doi.org/10.3390/s22249625.

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Infrastructures must be inspected regularly to ensure serviceability and public safety. In the case of the Thailand expressway, 200 km of an elevated structure must be inspected once a year. Thailand expressway is an elevated reinforced concrete structure. Visual inspection for defects and structural movements such as excessive deflections, transverse movements, or settlements is a cumbersome process. Therefore, a mobile mapping 3D laser scanning (MLS) which is a high-resolution 3D laser scanner (Trimble MX-8) equipped on a vehicle, was introduced. Scanning was performed on live traffic on the expressway. From MLS, both the structure geometry and pavement point cloud data were obtained. A good agreement between elevations of the Rama XI bridge in Bangkok measured by point cloud data using MLS and by a real-time kinematic survey was obtained. The effect of mesh size on the output by MLS was investigated. It was found that a mesh size of 10 cm reduced the computational effort by 75% when compared to a mesh size of 5 cm. However, the International Roughness Index was reduced by 5%. International Roughness Index (IRI) estimated by MLS was close to the IRI values measured by the profilometer. However, a significant overestimation in the case of rutting depth was observed.

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Sui, Lichun, Jianfeng Zhu, Mianqing Zhong, Xue Wang y Junmei Kang. "Extraction of road boundary from MLS data using laser scanner ground trajectory". Open Geosciences 13, n.º 1 (1 de enero de 2021): 690–704. http://dx.doi.org/10.1515/geo-2020-0264.

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Abstract Various means of extracting road boundary from mobile laser scanning data based on vehicle trajectories have been investigated. Independent of positioning and navigation data, this study estimated the scanner ground track from the spatial distribution of the point cloud as an indicator of road location. We defined a typical edge block consisting of multiple continuous upward fluctuating points by abrupt changes in elevation, upward slope, and road horizontal slope. Subsequently, such edge blocks were searched for on both sides of the estimated track. A pseudo-mileage spacing map was constructed to reflect the variation in spacing between the track and edge blocks over distance, within which road boundary points were detected using a simple linear tracking model. Experimental results demonstrate that the ground trajectory of the extracted scanner forms a smooth and continuous string just on the road; this can serve as the basis for defining edge block and road boundary tracking algorithms. The defined edge block has been experimentally verified as highly accurate and strongly noise resistant, while the boundary tracking algorithm is simple, fast, and independent of the road boundary model used. The correct detection rate of the road boundary in two experimental data is more than 99.2%.

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Hyyppä, Eric, Xiaowei Yu, Harri Kaartinen, Teemu Hakala, Antero Kukko, Mikko Vastaranta y Juha Hyyppä. "Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests". Remote Sensing 12, n.º 20 (13 de octubre de 2020): 3327. http://dx.doi.org/10.3390/rs12203327.

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In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

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Stăncioiu, Petru Tudor, Ioan Dutcă, Sergiu Constantin Florea y Marius Paraschiv. "Measuring Distances and Areas under Forest Canopy Conditions—A Comparison of Handheld Mobile Laser Scanner and Handheld Global Navigation Satellite System". Forests 13, n.º 11 (11 de noviembre de 2022): 1893. http://dx.doi.org/10.3390/f13111893.

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Measuring distances and areas under forest canopy conditions is often required for a broad range of forest research and management-related activities. While modern technologies, such as handheld mobile laser scanning (MLS), made possible the tridimensional representation of forests with great accuracy, the practical application is still limited by its high costs and challenging data processing. The handheld Global Navigation Satellite System (GNSS) represents the classical alternative, determining the distances and areas based on point coordinates. In this study, we aimed to assess the accuracy of a handheld GNSS, relative to the handheld MLS, in measuring distances and areas under forest canopy conditions. The material consists of 209 ant nests, which were mapped in a mixed-species deciduous forest of North-Eastern Romania. The GNSS- and MLS-based distances among nests were compared using the Bland–Altman plots. The differences in size and shape of the areas described by the nests were analyzed using (i) the shape compactness and (ii) the form factor of the convex polygons. In general, the GNSS-based distances were shorter compared with those based on MLS. However, for most cases, the intervals of agreement between the two instruments were within the limits of GNSS accuracy (i.e., ±10 m). The largest mean differences occurred when nests were in dense canopy conditions and on rugged terrain. The GNSS-based area of the convex polygons was smaller in most cases, but no significant correlation between the size of the area and the size of the relative difference was found. Furthermore, both the shape compactness and the form factor of the polygons were also smaller for the GNSS-based method compared with the MLS-based method, with differences up to 10%. In conclusion, measurements recorded by GNSS were less accurate, and under certain forest conditions (dense canopies, rugged terrain), large systematic errors can occur and therefore limit its use.

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Shokri, D., M. Zaboli, F. Dolati y S. Homayouni. "POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (14 de enero de 2023): 721–27. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-721-2023.

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Abstract. Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations.

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Fu, Yongjian, Zongchun Li, Wenqi Wang, Hua He, Feng Xiong y Yong Deng. "Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor". Sensors 21, n.º 7 (1 de abril de 2021): 2431. http://dx.doi.org/10.3390/s21072431.

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To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

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21

Honma, R., H. Date y S. Kanai. "EXTRACTION OF ROAD EDGES FROM MLS POINT CLOUDS USING BEND ANGLE OF SCANLINES". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14 de agosto de 2020): 1091–97. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1091-2020.

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Abstract. Efficient road edge extraction from point clouds acquired by Mobile Laser Scanning (MLS) is an important task because the road edge is one of the main elements of high definition maps. In this paper, we present a scanline-based road edge extraction method using a bend angle of scanlines from MLS point clouds. Scanline-based methods have advantages in that computational cost is low, it is easy to extract accurate road edges, and they are independent of driving speed of MLS compared to methods using unorganized points. In contrast, there are some problems with these methods where the extraction accuracy becomes low at curb cuts and intersections. The extraction accuracy becomes low caused by the scanning noise and small occlusion from weeds and fallen leaves. In addition, some parameters should be adjusted according to the mounting angle of the laser scanner on the vehicle. Therefore, we present a scanline-based road edge extraction method which can solve these problems. First, the points of the scanline are projected to a plane in order to reduce the influence of the mounting angle of the laser scanner on the vehicle. Next, the bend angle of each point is calculated by using filtered point clouds which are not vulnerable to small occlusions around the curb such as weeds. Then, points with a local maximum of bend angle and close to trajectories are extracted as seed points. Finally, road edges are generated by tracking based on bend angle of scanlines and smoothness of road edges from the seed points. In the experiments, our proposed methods achieved a completeness of over 95.3%, a correctness of over 95.0%, a quality of over 90.7%, and RMS difference less than 18.7 mm in total.

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22

Oude Elberink, S. y B. Kemboi. "User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (11 de agosto de 2014): 239–46. http://dx.doi.org/10.5194/isprsarchives-xl-3-239-2014.

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This paper describes a method that aims to find all instances of a certain object in Mobile Laser Scanner (MLS) data. In a userassisted approach, a sample segment of an object is selected, and all similar objects are to be found. By selecting samples from multiple classes, a classification can be performed. Key assumption in this approach is that a one-to-one relationship exists between segments and objects. In this paper the focus is twofold: (1) to explain how to get proper segments, and (2) to describe how to find similar objects. Point attributes that help separating neighbouring objects are presented. These point attributes are used in an attributed connected component algorithm where segments are grown, based on proximity and attribute values. Per component, a feature vector is proposed that consists of two parts. The first is a height histogram, containing information on the height distribution of points within a component. The second contains size and shape information, based on the components’ bounding box. A simple correlation function is used to find similarities between samples, as selected by a user, and other components. Our approach is tested on a MLS dataset, containing over 300 objects in 13 classes. Detection accuracies heavily depend on the success of the segmentation, and the number of selected samples in combination with the variety of object types in the scene.

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23

Yang, Yuchen, Yung-Tsang Chen, Craig Hancock, Nicholas Hamm y Zhiang Zhang. "A Novel Approach for As-Built BIM Updating Using Inertial Measurement Unit and Mobile Laser Scanner". Remote Sensing 16, n.º 15 (26 de julio de 2024): 2743. http://dx.doi.org/10.3390/rs16152743.

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Building Information Modeling (BIM) has recently been widely applied in the Architecture, Engineering, and Construction Industry (AEC). BIM graphical information can provide a more intuitive display of the building and its contents. However, during the Operation and Maintenance (O&M) stage of the building lifecycle, changes may occur in the building’s contents and cause inaccuracies in the BIM model, which could lead to inappropriate decisions. This study aims to address this issue by proposing a novel approach to creating 3D point clouds for updating as-built BIM models. The proposed approach is based on Pedestrian Dead Reckoning (PDR) for an Inertial Measurement Unit (IMU) integrated with a Mobile Laser Scanner (MLS) to create room-based 3D point clouds. Unlike conventional methods previously undertaken where a Terrestrial Laser Scanner (TLS) is used, the proposed approach utilizes low-cost MLS in combination with IMU to replace the TLS for indoor scanning. The approach eliminates the process of selecting scanning points and leveling of the TLS, enabling a more efficient and cost-effective creation of the point clouds. Scanning of three buildings with varying sizes and shapes was conducted. The results indicated that the proposed approach created room-based 3D point clouds with centimeter-level accuracy; it also proved to be more efficient than the TLS in updating the BIM models.

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Nikoohemat, S., M. Peter, S. Oude Elberink y G. Vosselman. "EXPLOITING INDOOR MOBILE LASER SCANNER TRAJECTORIES FOR SEMANTIC INTERPRETATION OF POINT CLOUDS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (14 de septiembre de 2017): 355–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-355-2017.

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The use of Indoor Mobile Laser Scanners (IMLS) for data collection in indoor environments has been increasing in the recent years. These systems, unlike Terrestrial Laser Scanners (TLS), collect data along a trajectory instead of at discrete scanner positions. In this research, we propose several methods to exploit the trajectories of IMLS systems for the interpretation of point clouds. By means of occlusion reasoning and use of trajectory as a set of scanner positions, we are capable of detecting openings in cluttered indoor environments. In order to provide information about both the partitioning of the space and the navigable space, we use the voxel concept for point clouds. Furthermore, to reconstruct walls, floor and ceiling we exploit the indoor topology and plane primitives. The results show that the trajectory is a valuable source of data for feature detection and understanding of indoor MLS point clouds.

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25

Chen, Chao, Llewellyn Tang, Craig Matthew Hancock y Penghe Zhang. "Development of low-cost mobile laser scanning for 3D construction indoor mapping by using inertial measurement unit, ultra-wide band and 2D laser scanner". Engineering, Construction and Architectural Management 26, n.º 7 (19 de agosto de 2019): 1367–86. http://dx.doi.org/10.1108/ecam-06-2018-0242.

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Purpose The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping. Design/methodology/approach This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method. Findings The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study. Research limitations/implications As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method. Practical implications Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations. Originality/value The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

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Che, Erzhuo y Michael Olsen. "An Efficient Framework for Mobile Lidar Trajectory Reconstruction and Mo-norvana Segmentation". Remote Sensing 11, n.º 7 (8 de abril de 2019): 836. http://dx.doi.org/10.3390/rs11070836.

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Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the point clouds, processing MLS data can be still challenging with respect to effectiveness, efficiency, and versatility. This paper proposes an efficient MLS data processing framework for general purposes consisting of three main steps: trajectory reconstruction, scan pattern grid generation, and Mo-norvana (Mobile Normal Variation Analysis) segmentation. We present a novel approach to reconstructing the scanner trajectory, which can then be used to structure the point cloud data into a scan pattern grid. By exploiting the scan pattern grid, point cloud segmentation can be performed using Mo-norvana, which is developed based on our previous work for processing Terrestrial Laser Scanning (TLS) data, normal variation analysis (Norvana). In this work, with an unorganized MLS point cloud as input, the proposed framework can complete various tasks that may be desired in many applications including trajectory reconstruction, data structuring, data visualization, edge detection, feature extraction, normal estimation, and segmentation. The performance of the proposed procedures are experimentally evaluated both qualitatively and quantitatively using multiple MLS datasets via the results of trajectory reconstruction, visualization, and segmentation. The efficiency of the proposed method is demonstrated to be able to handle a large dataset stably with a fast computation speed (about 1 million pts/sec. with 8 threads) by taking advantage of parallel programming.

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Mohd Rapheal, M. S. A., A. Farhana, M. R. Mohd Salleh, M. Z. Abd Rahman, Z. Majid, I. A. Musliman, A. F. Abdullah y Z. Abd Latif. "MACHINE LEARNING APPROACH FOR TENAGA NASIONAL BERHAD (TNB) OVERHEAD POWERLINE AND ELECTRICITY POLE INVENTORY USING MOBILE LASER SCANNING DATA". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W3-2021 (11 de enero de 2022): 239–46. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w3-2021-239-2022.

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Abstract. Electricity assets recognition and inventory is a fundamental task in the geospatial-based electrical power distribution management. In Malaysia, Tenaga Nasional Berhad (TNB) aims to complete their assets inventory throughout the country by 2022. Previous research has shown that a method for assets detection especially for TNB is still at an early stage, which mainly relied on manual extraction of the assets from different data sources including mobile laser scanner (MLS). This research aims at evaluating a geospatial method based on machine learning to classify the TNB assets using high density MLS data. The MLS data was collected using Riegl VMQ-1 HA scanner and supported by the base station and control points for point cloud registration purpose. In the first stage the point clouds were classified into ground and non-ground objects. The non-ground points were further classified into different landcover types i.e. vegetation, building, and other classes. The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software. Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments) were estimated. This information was validated using field collected reference data. The results show that the detection accuracy for electricity poles and overhead power line are 65% and 63% respectively. The estimation of length, diameter and height of the spun pole from point clouds has produced Root Mean Square Error (RMSE) value of 0.081cm, 0.263 cm and 0.372 cm respectively. Meanwhile for the concrete pole, the length, diameter and height has been successfully estimated with the value of RMSE of 0.034 cm, 0.029 cm and 0.331 cm respectively. The length of overhead powerline was estimated with 59.02 cm RMSE. In conclusion, the MLS data had show promising results for a semi-automatic detection and characterization of TNB overhead powerlines and poles in the sub-urban area. Such outcome can be used to support the inventory and maintenance process of the TNB assets.

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Nikoohemat, Shayan, Michael Peter, Sander Oude Elberink y George Vosselman. "Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories". Remote Sensing 10, n.º 11 (7 de noviembre de 2018): 1754. http://dx.doi.org/10.3390/rs10111754.

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The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes.

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Keitaanniemi, Aino, Antero Kukko, Juho-Pekka Virtanen y Matti T. Vaaja. "Measurement Strategies for Street-Level SLAM Laser Scanning of Urban Environments". Photogrammetric Journal of Finland 27, n.º 1 (2020): 1–19. http://dx.doi.org/10.17690/020271.1.

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Data collection for street-level mapping is currently executed with terrestrial (TLS) or mobile laser scanners (MLS). However, these methods have disadvantages such as TLS requiring a lot of time and MLS being dependent on the global navigation satellite system (GNSS) and an inertial measurement unit (IMU). These are not problems if we use simultaneous localization and mapping (SLAM) based laser scanners. We studied the utility of a SLAM ZEB-REVO scanner for mapping street-level objects in an urban environment by analyzing the geometric and visual differences with a TLS reference. In addition to this, we examined the influence of traffic on the measurement strategy. The results of the study showed that SLAM-based laser scanners can be used for street-level mapping. However, the measurement strategy affects the point clouds. The strategy of walking trajectory in loops produced a 2 cm RMS and 4-6 mm mode of error even in not optimal situations of the sensor in the urban environment. However, it was possible to get an RMS under 2.2 cm and a 32 cm mode of error with other measurement strategies.

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Russhakim, N. A. S., M. F. M. Ariff, Z. Majid, K. M. Idris, N. Darwin, M. A. Abbas, K. Zainuddin y A. R. Yusoff. "THE SUITABILITY OF TERRESTRIAL LASER SCANNING FOR BUILDING SURVEY AND MAPPING APPLICATIONS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (31 de enero de 2019): 663–70. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-663-2019.

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Abstract. The popularity of Terrestrial Laser Scanner (TLS) has been introduced into a field of surveying and has increased dramatically especially in producing the 3D model of the building. The used of terrestrial laser scanning (TLS) is becoming rapidly popular because of its ability in several applications, especially the ability to observe complex documentation of complex building and observe millions of point cloud in three-dimensional in a short period. Users of building plan usually find it difficult to translate the traditional two-dimensional (2D) data on maps they see on a flat piece of paper to three-dimensional (3D). The TLS is able to record thousands of point clouds which contains very rich of geometry details and made the processing usually takes longer time. In addition, the demand of building survey work has made the surveyors need to obtain the data with full of accuracy and time saves. Therefore, the aim of this study is to study the limitation uses of TLS and its suitability for building survey and mapping. In this study, the efficiency of TLS Leica C10 for building survey was determined in term of its accuracy and comparing with Zeb-Revo Handheld Mobile Laser Scanning (MLS) and the distometer. The accuracy for scanned data from both, TLS and MLS were compared with the Distometer by using root mean square error (RMSE) formula. Then, the 3D model of the building for both data, TLS and MLS were produced to analyze the visualization for different type of scanners. The software used; Autodesk Recap, Autodesk Revit, Leica Cyclone Software, Autocad Software and Geo Slam Software. The RMSE for TLS technique is 0.001m meanwhile, RMSE for MLS technique is 0.007m. The difference between these two techniques is 0.006m. The 3D model of building for both models did not have too much different but the scanned data from TLS is much easier to process and generate the 3D model compared to scanned data from MLS. It is because the scanned data from TLS comes with an image, while none from MLS scanned data. There are limitations of TLS for building survey such as water and glass window but this study proved that acquiring data by TLS is better than using MLS.

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Tyagur, N. y M. Hollaus. "DIGITAL TERRAIN MODELS FROM MOBILE LASER SCANNING DATA IN MORAVIAN KARST". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (9 de junio de 2016): 387–94. http://dx.doi.org/10.5194/isprs-archives-xli-b3-387-2016.

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During the last ten years, mobile laser scanning (MLS) systems have become a very popular and efficient technology for capturing reality in 3D. A 3D laser scanner mounted on the top of a moving vehicle (e.g. car) allows the high precision capturing of the environment in a fast way. Mostly this technology is used in cities for capturing roads and buildings facades to create 3D city models. In our work, we used an MLS system in Moravian Karst, which is a protected nature reserve in the Eastern Part of the Czech Republic, with a steep rocky terrain covered by forests. For the 3D data collection, the Riegl VMX 450, mounted on a car, was used with integrated IMU/GNSS equipment, which provides low noise, rich and very dense 3D point clouds. The aim of this work is to create a digital terrain model (DTM) from several MLS data sets acquired in the neighbourhood of a road. The total length of two covered areas is 3.9 and 6.1 km respectively, with an average width of 100 m. For the DTM generation, a fully automatic, robust, hierarchic approach was applied. The derivation of the DTM is based on combinations of hierarchical interpolation and robust filtering for different resolution levels. For the generation of the final DTMs, different interpolation algorithms are applied to the classified terrain points. The used parameters were determined by explorative analysis. All MLS data sets were processed with one parameter set. As a result, a high precise DTM was derived with high spatial resolution of 0.25 x 0.25 m. The quality of the DTMs was checked by geodetic measurements and visual comparison with raw point clouds. The high quality of the derived DTM can be used for analysing terrain changes and morphological structures. Finally, the derived DTM was compared with the DTM of the Czech Republic (DMR 4G) with a resolution of 5 x 5 m, which was created from airborne laser scanning data. The vertical accuracy of the derived DTMs is around 0.10 m.

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Shao, J., W. Zhang, L. Luo, S. Cai y H. Jiang. "SLAM-BASED BACKPACK LASER SCANNING FOR FOREST PLOT MAPPING". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (3 de agosto de 2020): 267–71. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-267-2020.

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Abstract. Acquisition of three-dimensional (3D) structural information is significant for forest measurements. To achieve faster data collection in forests, we design a backpack laser scanning (BLS) system using a single mobile laser scanning (MLS) scanner and specific to forest environments. The simultaneous localization and mapping (SLAM) approach based on the natural geometric characteristics of trees is used for BLS-based forest mapping, in which the skeleton line of the individual tree is extracted for scan matching and the incremental maps are adopted for global optimization of all the BLS point clouds. The final experimental results show that the SLAM-based BLS system achieves accurate forest plots mapping and allows reaching low mapping errors, in which the mean errors are approximately 3 cm in the horizontal and 2 cm in the vertical direction.

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33

Tyagur, N. y M. Hollaus. "DIGITAL TERRAIN MODELS FROM MOBILE LASER SCANNING DATA IN MORAVIAN KARST". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (9 de junio de 2016): 387–94. http://dx.doi.org/10.5194/isprsarchives-xli-b3-387-2016.

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During the last ten years, mobile laser scanning (MLS) systems have become a very popular and efficient technology for capturing reality in 3D. A 3D laser scanner mounted on the top of a moving vehicle (e.g. car) allows the high precision capturing of the environment in a fast way. Mostly this technology is used in cities for capturing roads and buildings facades to create 3D city models. In our work, we used an MLS system in Moravian Karst, which is a protected nature reserve in the Eastern Part of the Czech Republic, with a steep rocky terrain covered by forests. For the 3D data collection, the Riegl VMX 450, mounted on a car, was used with integrated IMU/GNSS equipment, which provides low noise, rich and very dense 3D point clouds. The aim of this work is to create a digital terrain model (DTM) from several MLS data sets acquired in the neighbourhood of a road. The total length of two covered areas is 3.9 and 6.1 km respectively, with an average width of 100 m. For the DTM generation, a fully automatic, robust, hierarchic approach was applied. The derivation of the DTM is based on combinations of hierarchical interpolation and robust filtering for different resolution levels. For the generation of the final DTMs, different interpolation algorithms are applied to the classified terrain points. The used parameters were determined by explorative analysis. All MLS data sets were processed with one parameter set. As a result, a high precise DTM was derived with high spatial resolution of 0.25 x 0.25 m. The quality of the DTMs was checked by geodetic measurements and visual comparison with raw point clouds. The high quality of the derived DTM can be used for analysing terrain changes and morphological structures. Finally, the derived DTM was compared with the DTM of the Czech Republic (DMR 4G) with a resolution of 5 x 5 m, which was created from airborne laser scanning data. The vertical accuracy of the derived DTMs is around 0.10 m.

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Hillemann, M., J. Meidow y B. Jutzi. "IMPACT OF DIFFERENT TRAJECTORIES ON EXTRINSIC SELF-CALIBRATION FOR VEHICLE-BASED MOBILE LASER SCANNING SYSTEMS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (17 de septiembre de 2019): 119–25. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-119-2019.

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Abstract. The extrinsic calibration of a Mobile Laser Scanning system aims to determine the relative orientation between a laser scanner and a sensor that estimates the exterior orientation of the sensor system. The relative orientation is one component that limits the accuracy of a 3D point cloud which is captured with a Mobile Laser Scanning system. The most efficient way to determine the relative orientation of a Mobile Laser Scanning system is using a self-calibration approach as this avoids the need to perform an additional calibration beforehand. Instead, the system can be calibrated automatically during data acquisition. The entropy-based self-calibration fits into this category and is utilized in this contribution. In this contribution, we analyze the impact of four different trajectories on the result of the entropy-based self-calibration, namely (i) uni-directional, (ii) ortho-directional, (iii) bi-directional, and (iv) multi-directional trajectory. Theoretical considerations are supported by experiments performed with the publicly available MLS 1 – TUM City Campus data set. The investigations show that strong variations of the yaw angle in a confined space or bidirectional trajectories as well as the variation of the height of the laser scanner are beneficial for calibration.

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Zang, Yufu, Fancong Meng, Roderik Lindenbergh, Linh Truong-Hong y Bijun Li. "Deep Localization of Static Scans in Mobile Mapping Point Clouds". Remote Sensing 13, n.º 2 (10 de enero de 2021): 219. http://dx.doi.org/10.3390/rs13020219.

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Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

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Zang, Yufu, Fancong Meng, Roderik Lindenbergh, Linh Truong-Hong y Bijun Li. "Deep Localization of Static Scans in Mobile Mapping Point Clouds". Remote Sensing 13, n.º 2 (10 de enero de 2021): 219. http://dx.doi.org/10.3390/rs13020219.

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Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

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Chiappini, Stefano, Mattia Balestra, Federico Giulioni, Ernesto Marcheggiani, Eva Savina Malinverni y Roberto Pierdicca. "Comparing the accuracy of 3D urban olive tree models detected by smartphone using LiDAR sensor, photogrammetry and NeRF: a case study of ’Ascolana Tenera’ in Italy". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-3-2024 (4 de noviembre de 2024): 61–68. http://dx.doi.org/10.5194/isprs-annals-x-3-2024-61-2024.

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Abstract. Rapid urban growth makes green space management crucial to improve citizens’ well-being. Urban olive trees characterize the Italian landscapes and their culture. This study explores different methodologies for urban tree assessment in this context, using an iPhone 14 Pro Max. These included: 1) its integrated Light Detection and Ranging (LiDAR) sensor using the Recon3D app, 2) its camera with Structure from Motion (SfM) techniques, and 3) its camera for generating 3D models using Neural Radiance Fields (NeRF). Additionally, a professional Mobile Laser Scanner (MLS), was used for comparison. Total height (H), canopy base height (CBH) and canopy volume (CV) measurements were extracted using both CloudCompare and allometric formulas. The main aim of this paper is to compare the 3D models of olive trees obtained from low-cost sensors with those generated from the MLS, which is a more accurate device but comes with significantly higher costs. The results, in terms of RMSE (iPhone LiDAR - H: 0.46 m, CBH: 0.12 m, CV: 15.66 m3; iPhone-SfM - H: 0.95 m, CBH: 0.19 m, CV: 25.85 m3; iPhone-NeRF - H: 1.26 m, CBH: 0.31 m, CV: 33.79 m3), bias and volume differences, reveal that the smartphone, in all the methodologies, tends to underestimate measurements as the size of the trees increases. This is due to the higher MLS range of acquisition. Despite these limitations, low-cost solutions like smartphone-based methods can be a viable alternative given their economic accessibility.

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Ghorbani, Fariborz, Hamid Ebadi, Norbert Pfeifer y Amin Sedaghat. "Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure". Remote Sensing 14, n.º 16 (21 de agosto de 2022): 4099. http://dx.doi.org/10.3390/rs14164099.

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Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%.

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Mohammed, H., N. M. Alsubaie, M. Elhabiby y N. El-sheimy. "Registration of time of flight terrestrial laser scanner data for stop-and-go mode". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (7 de noviembre de 2014): 287–91. http://dx.doi.org/10.5194/isprsarchives-xl-1-287-2014.

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Terrestrial Laser Scanners (TLS) are utilized through different data acquisition techniques such as Mobile Laser Scanning (MLS) and the output can be used in different applications such as 3D city modelling, cultural heritage documentations, oil and Gas as built, etc... In this research paper, we will investigate one of the modes of TLS on mobile mapping platform. Namely the Stop-and-Go (SAG) mode. Unlike the continuous mode, the Stop-and-Go mode does not require the use of IMU to estimate the TLS attitude and thus inturn it has an overall reduction in the system cost. Moreover, it decreases the time required for data processing in comparison with the continuous mode. For successful use of SAG mobile mapping in urban areas, it is preferred to use a long range time of flight laser scanner to cover long distances in each scan and minimize the registration error. The problem arise with Long range laser scanners is their low point cloud density. The low point cloud density affects the registration accuracy specially in monitoring applications. The point spacing between points is one of the issues facing the registration especially when the matching points are chosen manually. Since most of TLS nowadays are equipped with camera on-board we can utilize the camera to get an initial estimate of the registration parameters based on image matching. After having an initial approximation of the registration parameters we feed those parameters to the Iterative Closest Point algorithm to obtain more accurate registration result.

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La Placa, S. y E. Doria. "RELIABILITY OF DTMS OBTAINED WITH MOBILE FAST SURVEYS TECHNIQUES". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (25 de febrero de 2022): 299–306. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-299-2022.

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Abstract. The contribution addresses the issue of the integrated survey aimed at three-dimensional modeling for the documentation of different types of terrain through the analysis of two case studies located in the province of Pavia - Italy. The techniques of aerial photogrammetric acquisition SfM (UAVs), Terrestrial Laser Scanner (TLS) and Mobile (MLS) are now consolidated and widely used, managing to meet the needs of documentation of land levelling, monitoring, and analysis of landslide volumes. The two case studies present difficulties due to a strong inclination of the land and extensive presence of vegetation in the first case and to a strong presence of agricultural canalizations in the second case. The data processing phase focused on the comparison between MLS and close-range photogrammetry, while the acquisitions from TLS were used as control data. This acquisition method allows avoiding the process of approximation and reconstruction of the DTM under the vegetation, ensuring the correctness of the data relating to the ground course. The database allows the generation of highly reliable DTMs using specific point cloud modeling and processing software. Fast survey instruments are ideal in large areas or in hilly areas where sub-vertical sections and covered by vegetation are often present, difficult to detect only with close-range photogrammetry.

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Balado, J., L. Díaz-Vilariño, P. Arias y I. Garrido. "POINT CLOUDS TO INDOOR/OUTDOOR ACCESSIBILITY DIAGNOSIS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (13 de septiembre de 2017): 287–93. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-287-2017.

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This work presents an approach to automatically detect structural floor elements such as steps or ramps in the immediate environment of buildings, elements that may affect the accessibility to buildings. The methodology is based on Mobile Laser Scanner (MLS) point cloud and trajectory information. First, the street is segmented in stretches along the trajectory of the MLS to work in regular spaces. Next, the lower region of each stretch (the ground zone) is selected as the ROI and normal, curvature and tilt are calculated for each point. With this information, points in the ROI are classified in horizontal, inclined or vertical. Points are refined and grouped in structural elements using raster process and connected components in different phases for each type of previously classified points. At last, the trajectory data is used to distinguish between road and sidewalks. Adjacency information is used to classify structural elements in steps, ramps, curbs and curb-ramps. The methodology is tested in a real case study, consisting of 100&thinsp;m of an urban street. Ground elements are correctly classified in an acceptable computation time. Steps and ramps also are exported to GIS software to enrich building models from Open Street Map with information about accessible/inaccessible entrances and their locations.

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Zemerova, Angelina A. "METHODOLOGY FOR CREATING DIGITAL PROJECTS FOR THE RECONSTRUCTION AND REPAIR OF RAILWAY TRACKS". Interexpo GEO-Siberia 1 (21 de mayo de 2021): 130–36. http://dx.doi.org/10.33764/2618-981x-2021-1-130-136.

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The article describes a universal technique for creating digital projects for any road construction equipment equipped with an automated control system (SAU-3D), which includes surveying the repair route with the hardware and software complex (APK) "Profile" and a mobile laser scanner (MLS) "Scanput", data processing of a traditional project for repairs (longitudinal profile, straightening diagram), and adjustment of the digital basis of the existing railway track with design data from traditional projects. When using this technique, the transition to coordinate methods is carried out, linear-angular parameters are excluded due to the use of coordinates. The specifics of creating digital track models and digital projects at Russian Railways have been studied. An assessment of the accuracy of design solutions using SAU-3D and their compliance with regulatory requirements for the reconstruction and repair of railway tracks is given.

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Portocarrero, Euler, Rocio Huaman y Victor Andre Ariza Flores. "Comparative evaluation of UAV photogrammetry and mobile laser scanner for flexible pavement failure detection in developing countries". E3S Web of Conferences 497 (2024): 02018. http://dx.doi.org/10.1051/e3sconf/202449702018.

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Flexible pavements constitute a critical infrastructure that, throughout its life cycle, faces degradation caused by climatic variables and traffic loads. This deterioration affects their mechanical properties, leading to cracks and failures that reduce their functionality and longevity. It is therefore imperative that advanced analytical methodologies are applied to identify the appropriate level of intervention to ensure their optimal serviceability. Recently, technological innovations have emerged that allow the detailed assessment of flexible pavements in an efficient manner, covering large areas in a short time. This research focuses on whether drone photogrammetry (UAV) or mobile laser scanning (MLS) is more appropriate for the diagnosis of surface imperfections in flexible pavements in the context of developing countries, as well as the impacts that its adoption could have on road assessment. The qualitative study is based on a literature review and uses the Choosing by Advantages (CBA) method to evaluate and compare the decisive qualities in the selection of technologies. The results indicate that the mobile laser scanner provides accurate topographic and geometric characterisation at the millimetre level. However, drone photogrammetry, standing out for its high flexibility, low cost and ease of operation, presents itself as the most viable solution for continuous road condition monitoring.

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Warchoł, A., T. Karaś y M. Antoń. "SELECTED QUALITATIVE ASPECTS OF LIDAR POINT CLOUDS: GEOSLAM ZEB-REVO AND FARO FOCUS 3D X130". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (19 de octubre de 2023): 205–12. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-205-2023.

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Abstract. This paper presents a comparison of LiDAR point clouds acquired using two, different measurement techniques: static TLS (Terrestrial Laser Scanning) performed with a FARO Focus3D X130 laser scanner and a SLAM-based (Simultaneous Localization and Mapping) unit of MLS (Mobile Laser Scanning), namely GeoSLAM ZEB-REVO. After the two point clouds were brought into a single coordinate system, they were compared with each other in terms of internal accuracy and density. The density aspect was visualized using 2D density rasters, and calculated using 3 methods available in CloudCompare software. Thus, one should consider before choosing how to acquire a LiDAR point cloud whether a short measurement time is more important (ZEB-REVO) or whether higher density and measurement accuracy is more important (FARO Focus3D X130). In BIM/HBIM modeling applications, logic dictates that the TLS solution should be chosen, despite the longer data acquisition and processing time, but with a cloud with far better quality parameters that allow objects on the point cloud to be recognized. In a situation where the TLS point cloud is 20 times more dense, it allows to model objects at the appropriate level of geometric detail.

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Oniga, V. E., L. Morelli, M. Macovei, C. Chirila, A. I. Breaban, F. Remondino y P. Sestraș. "PPK PROCESSING TO BOOST UAS ACCURACY IN CADASTRAL MAPPING". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (25 de mayo de 2023): 345–52. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-345-2023.

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Abstract. Unmanned Aerial Systems (UAS) are increasingly used in different applications, including 3D urban modelling, cadastral mapping, urban planning, GIS information system and other fields because of their advantages. As a consequence, UAS equipment is constantly developed to provide more accurate results in a more reliable mode. This paper aims to evaluate the performances of a low-cost UAS system, namely DJI Phantom 4 Pro v2 equipped with a TeoKIT GNSS PPK (post-processing kinematic) module for cadastral mapping purposes. Two fights (oblique and nadir) over a residential area at 60 m height were performed and some 100 ground points were used to derive RMSE accuracies. Comparison between GNSS-aided with PPK processing and indirect georeferencing processes are performed. Given a mobile laser scanner (MLS) point cloud as ground truth, comparison with UAS point clouds and manually digitized features are also performed and reported.

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del Río-Barral, P., J. Grandío, B. Riveiro y P. Arias. "IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (25 de mayo de 2023): 107–12. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023.

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Abstract. The maintenance of road infrastructures is one of the main challenges that transportation authorities must face to guarantee the safe mobility of people and goods. Novel remote monitoring technologies offer advanced solutions for this issue, allowing the inspection of large sections of the network in a time-effective way. In this paper, we introduce a methodology for the detection of cracks on road pavements using point clouds acquired with a mobile laser scanner. First, the points of the cloud are labelled as pavement or cracks based on field annotations, and local geometric features of the points are calculated using principal component analysis. Two different machine learning classifiers, Support Vector Machine (SVM) and Random Forest, are then trained to identify crack points using the point feature data. The crack points predicted by the classifiers are clustered as individual instances and compared to the corresponding ones from a test dataset. Although pointwise performance of the method is modest, it can correctly identify and measure areas of the pavement affected by cracking.

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Hetti Arachchige, N. y S. Perera. "Automatic modelling of building façade objects via primitive shapes". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (11 de agosto de 2014): 115–20. http://dx.doi.org/10.5194/isprsarchives-xl-3-115-2014.

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This paper presents a new approach to recognize individual façade objects and to reconstruct such objects in 3D using MLS point clouds. Core of the approach is a primitive shape based algorithm, which introduces building primitives, to identify the façade objects separately from other irrelevant objects and then to model the correct topology. The primitive shape is identified against defined different primitive shapes by using the Douglas-Peucker algorithm. The advantage of this process is that it offers an ability not only to model correct geometric shapes but also to remove occlusion effects from the final model. To evaluate the validity of the proposed approach, experiments have been conducted using two types of street scene point clouds captured by Optech Lynx Mobile Mapper System and Z+F laser scanner. Results of the experiments show that the completeness, correctness, and quality of the reconstructed building façade objects are well over 90 %, proving the proposed method is a promising solution for modelling 3D façade objects with different geometric shapes.

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Di Benedetto, A., M. Fiani, L. Petti y E. Repetto. "ROAD SURFACE MODELLING AND CHARACTERIZATION FROM TERRESTRIAL LIDAR DATA". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (25 de mayo de 2023): 113–20. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-113-2023.

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Abstract. The general purpose of the paper is the study of surveying and data processing methodologies that are efficient to obtain more detailed metric data on road infrastructures than can be derived from classical surveying techniques. The inspection and monitoring of the condition of an infrastructure are two essential steps to increase the users' safety and to properly manage the available resources and are a preparatory step to the subsequent steps of deciding on the interventions to be put in place. Analysis of the state of degradation, if conducted with traditional methodologies, can be risky and sometimes inefficient. The Mobile Laser Scanner (MLS) technique, based on LiDAR (Light Detection and Ranging) technology, is also widely used today as an alternative to traditional techniques since it allows obtaining dense and accurate point clouds of the road surface. The purpose of our work is to provide a workflow for the processing of MLS data aimed at producing some useful indicators to describe the functional and structural characteristics of the pavement, with the goal of optimizing the decision-making processes of the Managing Authority. Specifically, the data flow was studied, and several processing algorithms were implemented to identify and quantify surface defects and road roughness. The result of the entire process is the creation of an Atlas in QGIS to create graphical tables related to each individual cross profile and that can be used to identify all those sections that need emergency actions and therefore characterized by a high priority of intervention.

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Moradi, L., M. Saadatseresht y P. Shokrzadeh. "DEVELOPMENT OF A VOXEL BASED LOCAL PLANE FITTING FOR MULTI-SCALE REGISTRATION OF SEQUENTIAL MLS POINT CLOUDS". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (14 de enero de 2023): 523–30. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-523-2023.

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Abstract. The Mobile Laser Scanner (MLS) system is one of the most accurate and fastest data acquisition systems for indoor and outdoor environments mapping. Today, to use this system in an indoor environment where it is impossible to capture GNSS data, Simultaneous Localization and Mapping (SLAM) is used. Most SLAM research has used probabilistic approaches to determine the sensor position and create a map, which leads to drift error in the final result due to their uncertainty. In addition, most SLAM methods give less importance to geometry and mapping concepts. This research aims to solve the SLAM problem by considering the adjustment concepts in mapping and geometrical principles of the environment and proposing an algorithm for reducing drift. For this purpose, a model-based registration is suggested. Correspondence points fall in the same voxel by voxelization, and the registration process is done using a plane model. In this research, two pyramid and simple registration methods are proposed. The results show that the simple registration algorithm is more efficient than the pyramid when the distance between sequential scans is not large otherwise, the pyramid registration is used. In the evaluation, by using simulated data in both pyramid and simple methods, 96.9% and 97.6% accuracy were obtained, respectively. The final test compares the proposed method with a SLAM method and ICP algorithm, which are described further.

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Yao, W., P. Polewski y P. Krzystek. "SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (13 de septiembre de 2017): 971–76. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-971-2017.

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In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.

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