Journal articles on the topic 'Line-feature-based mapping'
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1
An, Su-Yong, Jeong-Gwan Kang, Lae-Kyoung Lee, and Se-Young Oh. "Line Segment-Based Indoor Mapping with Salient Line Feature Extraction." Advanced Robotics 26, no. 5-6 (January 2012): 437–60. http://dx.doi.org/10.1163/156855311x617452.
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Li Yunduo, 李运舵, 车进 Che Jin, and 薛澄 Xue Cheng. "基于点线特征匹配的实时定位及地图重建方法." Laser & Optoelectronics Progress 59, no. 2 (2022): 0210003. http://dx.doi.org/10.3788/lop202259.0210003.
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Sun, Lu Ping, Bo Qian, and Yan Zhi Guan. "Mapping Matrix of Variable Cross-Section Roll Forming Based on Visual Detection." Advanced Materials Research 683 (April 2013): 797–800. http://dx.doi.org/10.4028/www.scientific.net/amr.683.797.
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An image mapping algorithm is proposed to improve the precision of flexible roll forming. The mapping algorithm based on visual feedback and Image analysis technology. On-line detection device realizes image acquisition of cross-section profile. After processing and analysis, geometric feature is extracted by Hough transform in the image. Geometric feature is transformed to matrix which is built a mapping relation with the original matrix. The mapping algorithm describes the displacement and rotary error in the process of flexible roll forming. Closed loop control is realized by feeding back the error to the control system. Experiments results show that the mapping algorithm is a good algorithm for on-line dimensional precision detection in sheet metal forming.
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Zhang, Tong, Chunjiang Liu, Jiaqi Li, Minghui Pang, and Mingang Wang. "A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features." Drones 6, no. 1 (January 13, 2022): 23. http://dx.doi.org/10.3390/drones6010023.
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In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.
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Zhou, Fei, Limin Zhang, Chaolong Deng, and Xinyue Fan. "Improved Point-Line Feature Based Visual SLAM Method for Complex Environments." Sensors 21, no. 13 (July 5, 2021): 4604. http://dx.doi.org/10.3390/s21134604.
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Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.
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He, Xuan, Wang Gao, Chuanzhen Sheng, Ziteng Zhang, Shuguo Pan, Lijin Duan, Hui Zhang, and Xinyu Lu. "LiDAR-Visual-Inertial Odometry Based on Optimized Visual Point-Line Features." Remote Sensing 14, no. 3 (January 27, 2022): 622. http://dx.doi.org/10.3390/rs14030622.
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This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using the least square method, is proposed to provide a richer visual feature for the front-end of LVIO. Secondly, multi-frame LiDAR point clouds were projected into the visual frame for feature depth correlation. Thirdly, the initial estimation results of Visual-Inertial Odometry (VIO) were carried out to optimize the scanning matching accuracy of LiDAR. Finally, a factor graph based on Bayesian network is proposed to build the LVIO fusion system, in which GNSS factor and loop factor are introduced to constrain LVIO globally. The evaluations on indoor and outdoor datasets show that the proposed algorithm is superior to other state-of-the-art algorithms in real-time efficiency, positioning accuracy, and mapping effect. Specifically, the average RMSE of absolute trajectory in the indoor environment is 0.075 m and that in the outdoor environment is 3.77 m. These experimental results can prove that the proposed algorithm can effectively solve the problem of line feature mismatching and the accumulated error of local sensors in mobile carrier positioning.
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Wang, Runzhi, Kaichang Di, Wenhui Wan, and Yongkang Wang. "Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes." Sensors 18, no. 10 (October 20, 2018): 3559. http://dx.doi.org/10.3390/s18103559.
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In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.
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Wu, Jianfeng, Jian Xiong, and Hang Guo. "Improving robustness of line features for VIO in dynamic scene." Measurement Science and Technology 33, no. 6 (March 25, 2022): 065204. http://dx.doi.org/10.1088/1361-6501/ac547f.
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Abstract The point feature, whose dynamic robustness has been widely studied, dominates in the field of visual simultaneous localization and mapping (SLAM) or visual-inertial odometry (VIO). When discussing a dynamic scene, line features are not given enough attention. This paper proposes a pre-processing step for VIO to reduce the influence of lines upon dynamic objects on system robustness and merges it into a state-of-the-art optimization-based VIO pipeline. First, it is determined whether the line feature is a potential dynamic line based upon the result of the semantic segmentation, optical flow and re-projection error. Then, instead of filtering them out, the information matrixes of these line features in the optimization function is adjusted by a weight-based method according to their tracked size. A simulated challenged visual-inertial dataset is used to evaluate the proposed algorithm against other state-of-the-art methods. The results shows that proposed method can increase robustness to dynamic scenes and make the line-based map neater and more intuitive through avoiding the drawing of dynamic line features during the mapping procedure.
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Zhao, Qijian, Yanlong Cao, Ting Liu, Lifei Ren, and Jiangxin Yang. "Tolerance specification of the plane feature based on the axiomatic design." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (May 25, 2018): 1481–92. http://dx.doi.org/10.1177/0954406218772001.
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Tolerance specification involves selecting tolerance types for functional or assembly features to control the variation of features. General methods tend to formulate a frame to specify all the features of part, while the specification methods or reasoning rules for specific feature (point, line, plane, cylinder, etc.) are less studied. This paper focuses on the tolerance-type selection of the plane feature. The theory of axiomatic design is introduced to select the tolerance type for the plane feature, and the problem is interpreted as a redundant decoupled design. To achieve the functional requirements, design parameters and constraints of physics domain are determined. The mapping rules, which are between design parameters and functional requirements, are generated based on the independent axiom. Considering the large number of solutions of the design, the constraints such as cost and inspection methods are introduced to reduce the number of solutions. The minimum information axiom is introduced for the optimum mapping rules and the tolerance types are selected by the optimum mapping rules for the plane feature. Finally, the specification process is concluded and demonstrated by means of an example.
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Bian, Jiang, Xiaolong Hui, Xiaoguang Zhao, and Min Tan. "A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection." International Journal of Advanced Robotic Systems 16, no. 1 (January 1, 2019): 172988141882022. http://dx.doi.org/10.1177/1729881418820227.
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Employing unmanned aerial vehicles to conduct close proximity inspection of transmission tower is becoming increasingly common. This article aims to solve the two key problems of close proximity navigation—localizing tower and simultaneously estimating the unmanned aerial vehicle positions. To this end, we propose a novel monocular vision–based environmental perception approach and implement it in a hierarchical embedded unmanned aerial vehicle system. The proposed framework comprises tower localization and an improved point–line-based simultaneous localization and mapping framework consisting of feature matching, frame tracking, local mapping, loop closure, and nonlinear optimization. To enhance frame association, the prominent line feature of tower is heuristically extracted and matched followed by the intersections of lines are processed as the point feature. Then, the bundle adjustment optimization leverages the intersections of lines and the point-to-line distance to improve the accuracy of unmanned aerial vehicle localization. For tower localization, a transmission tower data set is created and a concise deep learning-based neural network is designed to perform real-time and accurate tower detection. Then, it is in combination with a keyframe-based semi-dense mapping to locate the tower with a clear line-shaped structure in 3-D space. Additionally, two reasonable paths are planned for the refined inspection. In experiments, the whole unmanned aerial vehicle system developed on Robot Operating System framework is evaluated along the paths both in a synthetic scene and in a real-world inspection environment. The final results show that the accuracy of unmanned aerial vehicle localization is improved, and the tower reconstruction is fast and clear. Based on our approach, the safe and autonomous unmanned aerial vehicle close proximity inspection of transmission tower can be realized.
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Zhang, Ka, Yehua Sheng, and Chun Ye. "Stereo Image Matching for Vehicle-Borne Mobile Mapping System Based on Digital Parallax Model." International Journal of Vehicular Technology 2011 (July 3, 2011): 1–11. http://dx.doi.org/10.1155/2011/326865.
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Considering automatic and effective stereo image matching for vehicle-borne mobile mapping system (VMMS), a new stereo image matching algorithm based on digital parallax model (DPM) is proposed in this paper. The new matching propagation strategy is designed in this algorithm, which includes two processes as DPM construction and parallax prediction. With some known matched points, the DPM of stereo image pairs is firstly constructed, and parameters for confirming conjugate epipolar line is also worked out. Then searching range during dense matching can be confirmed under constraints of DPM and epipolar line, which can improve matching speed and accuracy. Furthermore, to improve matching robustness, the computation model of similarity measurement combined with local structure feature and global color feature is designed. The new algorithm is applied to actual stereo images taken by VMMS to verify its validity. Experimental results show that the proposed approach has higher reliability and accuracy.
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Hu, Haochen, Boyang Li, Wenyu Yang, and Chih-Yung Wen. "A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies." Remote Sensing 14, no. 16 (August 9, 2022): 3857. http://dx.doi.org/10.3390/rs14163857.
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Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.
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Shen, Yubao, and Zhipeng Jiao. "A Novel Self-Positioning Based on Feature Map Creation and Laser Location Method for RBPF-SLAM." Journal of Robotics 2021 (December 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/9988916.
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Aiming at the high computational complexity of the traditional Rao-Blackwellized Particle Filtering (RBPF) method for simultaneous localization and Mapping (SLAM), an optimization method of RBPF-SLAM system is proposed, which is based on lidar and least square line segment feature extraction as well as raster, reliability mapping continuity. Validation test results show that less storage in constructing a map with this method is occupied, and the computational complexity is significantly reduced. The effect of noise data on feature data extraction results is effectively avoided. It also solves the problem of error accumulation caused by noninteger grid size movement of unmanned vehicle in time update stage based on Markov positioning scheme. The improved RBPF-SLAM method can enable the unmanned vehicle to construct raster map in real time, and the efficiency and accuracy of map construction are significantly improved.
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Jia, Ni Yun, and Guan Zhong Yang. "A Method for Verifying Traceability between Feature Model and Software Architecture." Advanced Materials Research 998-999 (July 2014): 1085–91. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1085.
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Feature modeling is a main stream technology in domain requirement analysis of software product line engineering. Establishing the traceability between feature model and software architecture plays the essential role in improving software quality. Based on Formal Concept Analysis technology, we proposed a method to verify traceability between feature model and software architecture. The method analyzed the constitution of the feature, defined feature model and software architecture function expression, constructed a concept lattice and presented several mapping criteria to analysis it. It is more applicable for higher complexity model, comparing to the other model traceability method. A case study is used to demonstrate the feasibility of the method.
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Chen, Kai, Kai Zhan, Fan Pang, Xiaocong Yang, and Da Zhang. "R-LIO: Rotating Lidar Inertial Odometry and Mapping." Sustainability 14, no. 17 (August 30, 2022): 10833. http://dx.doi.org/10.3390/su141710833.
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In this paper, we propose a novel simultaneous localization and mapping algorithm, R-LIO, which combines rotating multi-line lidar and inertial measurement unit. R-LIO can achieve real-time and high-precision pose estimation and map-building. R-LIO is mainly composed of four sequential modules, namely nonlinear motion distortion compensation module, frame-to-frame point cloud matching module based on normal distribution transformation by self-adaptive grid, frame-to-submap point cloud matching module based on line and surface feature, and loop closure detection module based on submap-to-submap point cloud matching. R-LIO is tested on public datasets and private datasets, and it is compared quantitatively and qualitatively to the four well-known methods. The test results show that R-LIO has a comparable localization accuracy to well-known algorithms as LIO-SAM, FAST-LIO2, and Faster-LIO in non-rotating lidar data. The standard algorithms cannot function normally with rotating lidar data. Compared with non-rotating lidar data, R-LIO can improve localization and mapping accuracy in rotating lidar data.
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Huang, Yongdong, Jianwei Yang, Sansan Li, and Wenzhen Du. "Polar radius integral transform for affine invariant feature extraction." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 01 (January 2017): 1750005. http://dx.doi.org/10.1142/s0219691317500059.
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Affine transform is to describe the same target at different viewpoints to obtain the relationship between images of approximate model. Affine invariant feature extraction plays an important role in object recognition and image registration. Firstly, the definition of polar radius integral transform (PRIT) is put forward by means of the characterization of affine transform mapping straight line into straight line, where PRIT computes the integral along the polar radius direction and converts images into closed curves which keep the same affine transform with original images. Secondly, in order to extract affine invariant feature, an affine invariant feature extraction algorithm is also given based on PRIT. The proposed algorithm can be used to combine contour-based methods with region-based methods. It has some advantages of fewer amounts of computations and feasibility of feature extraction for objects with several components. Finally, the capability of anti-noise (Gaussian noise, salt and pepper noise) of PRIT is discussed. The simulation experiment results show that PRIT can effectively extract the affine invariant features, but also the low order PRIT has very strong robustness to noise.
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Chen, Baifan, Siyu Li, Haowu Zhao, and Limei Liu. "Map Merging with Suppositional Box for Multi-Robot Indoor Mapping." Electronics 10, no. 7 (March 30, 2021): 815. http://dx.doi.org/10.3390/electronics10070815.
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For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.
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Fan, Wenzheng, Wenzhong Shi, Haodong Xiang, and Ke Ding. "A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System." Remote Sensing 11, no. 23 (November 26, 2019): 2789. http://dx.doi.org/10.3390/rs11232789.
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Over the last decade, increasing demands for building interior mapping have brought the challenge of effectively and efficiently acquiring geometric information. Most mobile mapping methods rely on the integration of Simultaneous Localization And Mapping (SLAM) and costly Inertial Measurement Units (IMUs). Meanwhile, the methods also suffer misalignment errors caused by the low-resolution inhomogeneous point clouds captured using multi-line Mobile Laser Scanners (MLSs). While point-based alignments between such point clouds are affected by the highly dynamic moving patterns of the platform, plane-based methods are limited by the poor quality of the planes extracted, which reduce the methods’ robustness, reliability, and applicability. To alleviate these issues, we proposed and developed a method for plane extraction from low-resolution inhomogeneous point clouds. Based on the definition of virtual scanlines and the Enhanced Line Simplification (ELS) algorithm, the method extracts feature points, generates line segments, forms patches, and merges multi-direction fractions to form planes. The proposed method reduces the over-segmentation fractions caused by measurement noise and scanline curvature. A dedicated plane-to-plane point cloud alignment workflow based on the proposed plane extraction method was created to demonstrate the method’s application. The implementation of the coarse-to-fine procedure and the shortest-path initialization strategy eliminates the necessity of IMUs in mobile mapping. A mobile mapping prototype was designed to test the performance of the proposed methods. The results show that the proposed workflow and hardware system achieves centimeter-level accuracy, which suggests that it can be applied to mobile mapping and sensor fusion.
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Zhu, Daixian, Kangkang Ji, Dong Wu, and Shulin Liu. "A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel." Sensors 22, no. 19 (September 30, 2022): 7437. http://dx.doi.org/10.3390/s22197437.
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Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.
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Queiroz, Gustavo Lopes, Gregory J. McDermid, Mir Mustafizur Rahman, and Julia Linke. "The Forest Line Mapper: A Semi-Automated Tool for Mapping Linear Disturbances in Forests." Remote Sensing 12, no. 24 (December 20, 2020): 4176. http://dx.doi.org/10.3390/rs12244176.
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Forest land-use planning and restoration requires effective tools for mapping and attributing linear disturbances such as roads, trails, and asset corridors over large areas. Most existing linear-feature databases are generated by heads-up digitizing. While suitable for cartographic purposes, these datasets often lack the fine spatial details and multiple attributes required for more demanding analytical applications. To address this need, we developed the Forest Line Mapper (FLM), a semi-automated software tool for mapping and attributing linear features using LiDAR-derived canopy height models. Accuracy assessments conducted in the boreal forest of Alberta, Canada showed that the FLM reliably predicts both the center line (polyline) and footprint (extent polygons) of a variety of linear-feature types including roads, pipelines, seismic lines, and power lines. Our analysis showed that FLM outputs were consistently more accurate than publicly available datasets produced by human photo-interpreters, and that the tool can be reliably deployed across large application areas. In addition to accurately delineating linear features, the FLM generates a variety of spatial attributes associated with line geometry and vegetation characteristics from input canopy height data. Our statistical evaluation indicates that spatial attributes generated by the FLM may be useful for studying and classifying linear features based on disturbance type and ground conditions. The FLM is open-source and freely available and is aimed to assist researchers and land managers working in forested environments everywhere.
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Zhang, Ning, and Yongjia Zhao. "Fast and Robust Monocular Visua-Inertial Odometry Using Points and Lines." Sensors 19, no. 20 (October 19, 2019): 4545. http://dx.doi.org/10.3390/s19204545.
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When the camera moves quickly and the image is blurred or the texture in the scene is missing, the Simultaneous Localization and Mapping (SLAM) algorithm based on point feature experiences difficulty tracking enough effective feature points, and the positioning accuracy and robustness are poor, and even may not work properly. For this problem, we propose a monocular visual odometry algorithm based on the point and line features and combining IMU measurement data. Based on this, an environmental-feature map with geometric information is constructed, and the IMU measurement data is incorporated to provide prior and scale information for the visual localization algorithm. Then, the initial pose estimation is obtained based on the motion estimation of the sparse image alignment, and the feature alignment is further performed to obtain the sub-pixel level feature correlation. Finally, more accurate poses and 3D landmarks are obtained by minimizing the re-projection errors of local map points and lines. The experimental results on EuRoC public datasets show that the proposed algorithm outperforms the Open Keyframe-based Visual-Inertial SLAM (OKVIS-mono) algorithm and Oriented FAST and Rotated BRIEF-SLAM (ORB-SLAM) algorithm, which demonstrates the accuracy and speed of the algorithm.
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Amami, Mustafa M. "Fast and Reliable Vision-Based Navigation for Real Time Kinematic Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 922–32. http://dx.doi.org/10.22214/ijraset.2022.40395.
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Abstract: Automatic Image Matching (AIM) is the term used to identify the automatic detection of corresponding points located on the overlapping areas of multiple images. AIM is extensively used with Mobile Mapping System (MMS) for different engineering applications, such as highway infrastructure mapping, monitoring of road surface quality and markings, telecommunication, emergency response, and collecting data for Geographical Information Systems (GIS). Robotics community and Simultaneous Localization And Mapping (SLAM) based applications are other important areas that require fact and welldistributed AIM for robust vision navigation solutions. Different robust feature detection methods are commonly used for AIM, such as Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). The performance of such techniques have been widely investigated and compared, showing high capability to provide reliable and precise results. However, these techniques are still limited to be used for real and nearly real time SLAM based applications, such as intelligent Robots and low-cost Unmanned Aircraft Vehicles (UAV) based on vision navigation. The main limitations of these AIM techniques are represented in the relatively long processing time and the random distribution of matched points over the common area between images. This paper works on overcoming these two limitations, providing extremely fast AIM with well- distributed common points for robust real time vision navigation. Digital image pyramid, Epipolar line and 2D transformation have been utilized for limiting the size of search windows significantly and determining the rotating angle and scale level of features, reducing the overall processing time considerably. Using limited number of well-distributed common points has also helped to speed up the automatic matching besides providing robust vision navigation solution. The idea has been tested with terrestrial MMS images, and surveying UAV aerial images. The results reflect the high capability of the followed technique in providing fast and robust AIM for real-time SLAM based applications. Keywords: Automatic Image Matching, Epipolar Line, Image Pyramid, SLAM, Vision Navigation, Real Time, Vision Navigation.
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Liu, Zhiying, Xiren Miao, Zhiqiang Xie, Hao Jiang, and Jing Chen. "Power Tower Inspection Simultaneous Localization and Mapping: A Monocular Semantic Positioning Approach for UAV Transmission Tower Inspection." Sensors 22, no. 19 (September 28, 2022): 7360. http://dx.doi.org/10.3390/s22197360.
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Realizing autonomous unmanned aerial vehicle (UAV) inspection is of great significance for power line maintenance. This paper introduces a scheme of using the structure of a tower to realize visual geographical positioning of UAV for tower inspection and presents a monocular semantic simultaneous localization and mapping (SLAM) framework termed PTI-SLAM (power tower inspection SLAM) to cope with the challenge of a tower inspection scene. The proposed scheme utilizes prior knowledge of tower component geolocation and regards geographical positioning as the estimation of transformation between SLAM and the geographic coordinates. To accomplish the robust positioning and semi-dense semantic mapping with limited computing power, PTI-SLAM combines the feature-based SLAM method with a fusion-based direct method and conveys a loosely coupled architecture of a semantic task and a SLAM task. The fusion-based direct method is specially designed to overcome the fragility of the direct method against adverse conditions concerning the inspection scene. Experiment results show that PTI-SLAM inherits the robustness advantage of the feature-based method and the semi-dense mapping ability of the direct method and achieves decimeter-level real-time positioning in the airborne system. The experiment concerning geographical positioning indicates more competitive accuracy compared to the previous visual approach and artificial UAV operating, demonstrating the potential of PTI-SLAM.
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Aduni Sulaiman, Rabatul, Dayang Norhayati A. Jawawi, and Shahliza Abd Halim. "Coverage-based Approach for Model-based Testing in Software Product Line." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 63. http://dx.doi.org/10.14419/ijet.v7i4.15.21373.
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Rapid Quality assurance is an important element in software testing in order to produce high quality products in Software Product Line (SPL). One of the testing techniques that can enhance product quality is Model-Based Testing (MBT). Due to MBT effectiveness in terms of reuse and potential to be adapted, this technique has become an efficient approach that is capable to handle SPL requirements. In this paper, the authors present an approach to manage variability and requirements by using Feature Model (FM) and MBT. This paper focuses on modelling the integration towards enhancing product quality and reducing testing effort. Further, the authors considered coverage criteria, including pairwise coverage, all-state coverage, and all-transition coverage, in order to improve the quality of products. For modelling purposes, the authors constructed a mapping model based on variability in FM and behaviour from statecharts. The proposed approach was validated using mobile phone SPL case study.
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Xu, Yongfeng. "Photogrammetry-based structural damage detection by tracking a visible laser line." Structural Health Monitoring 19, no. 1 (April 26, 2019): 322–36. http://dx.doi.org/10.1177/1475921719840354.
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Research works on photogrammetry have received tremendous attention in the past few decades. One advantage of photogrammetry is that it can measure displacement and deformation of a structure in a fully non-contact, full-field manner. As a non-destructive evaluation method, photogrammetry can be used to detect structural damage by identifying local anomalies in measured deformation of a structure. Numerous methods have been proposed to measure deformations by tracking exterior features of structures, assuming that the features can be consistently identified and tracked on sequences of digital images captured by cameras. Such feature-tracking methods can fail if the features do not exist on captured images. One feasible solution to the potential failure is to artificially add exterior features to structures. However, painting and mounting such features can introduce unwanted permanent surficial modifications, mass loads, and stiffness changes to structures. In this article, a photogrammetry-based structural damage detection method is developed, where a visible laser line is projected to a surface of a structure, serving as an exterior feature to be tracked; the projected laser line is massless and its existence is temporary. A laser-line-tracking technique is proposed to track the projected laser line on captured digital images. Modal parameters of a target line corresponding to the projected laser line can be estimated by conducting experimental modal analysis. By identifying anomalies in curvature mode shapes of the target line and mapping the anomalies to the projected laser line, structural damage can be detected with identified positions and sizes. An experimental investigation of the damage detection method was conducted on a damaged beam. Modal parameters of a target line corresponding to a projected laser line were estimated, which compared well with those from a finite element model of the damaged beam. Experimental damage detection results were validated by numerical ones from the finite element model.
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Castilla, Guillermo, Jennifer Hird, Bryce Maynes, Doug Crane, John Cosco, Jim Schieck, and Greg McDermid. "Broadening modern resource inventories: A new protocol for mapping natural and anthropogenic features." Forestry Chronicle 89, no. 05 (October 2013): 681–89. http://dx.doi.org/10.5558/tfc2013-121.
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Conventional forests inventories narrowly focus on timber attributes and often neglect other aspects that may be relevant for other purposes. In an effort to broaden the usefulness of these inventories, we introduce a new protocol based on softcopy photo-interpretation for efficiently capturing both natural and anthropogenic features across a variety of landscapes. Salient aspects of this protocol include (1) the combined use of polygon, point and line feature representation; (2) over 50 fields per attribute table; and (3) semi-automated quality control tools. We show an application example over a 3-km by 7-km sample area in central-eastern Alberta.
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Chen, X., M. Chen, Z. Wei, and R. Zhong. "MODELING MAIN BODY OF OVERCROSSING BRIDGE BASED ON VEHICLE-BORNE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 699–702. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-699-2017.
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Vehicle-borne laser scanning (VBLS) is widely used to collect urban data for various mapping and modelling systems. This paper proposes a strategy of feature extraction and 3d model reconstruction for main body of overcrossing bridges based on VBLS point clouds. As the bridges usually have a large span, and the clouds data is often affected by obstacles, we have to use round-trip cloud data to avoid missing part. To begin with, pick out the cloud of the bridge body by an interactive clip-box, and group points by scan-line, then sort the points by scanning angle on each scan line. Since the position under the vehicle have a fixed scan-angle, a virtual path can be obtained. Secondly, extract horizontal line segments perpendicular to the virtual path along adjacent scan-lines, and then cluster line segments into long line-strings, which represent the top and bottom edge. Finally, regularize the line-strings and build 3d surface model of the bridge body. Experimental studies have demonstrated its efficiency and accuracy in case of building bridge model. Modelling the stairs at the both end of the bridge will be the direction of the next step.
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Dong, Naixi, Ruijuan Chi, and Weitong Zhang. "LiDAR Odometry and Mapping Based on Semantic Information for Maize Field." Agronomy 12, no. 12 (December 7, 2022): 3107. http://dx.doi.org/10.3390/agronomy12123107.
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Agricultural environment mapping is the premise of the autonomous navigation of agricultural robots. Due to the undulating terrain and chaotic environment, it is challenging to accurately map the environmental maize field using existing LOAM (LiDAR odometry and mapping) methods. This paper proposes a LOAM method based on maize stalk semantic features for 6-DOF (degrees of freedom) pose estimation and field mapping with agricultural robots operating in a dynamic environment. The piecewise plane fitting method filters the ground points for the complex farmland terrain. To eliminate the unstable factors in the environment, we introduce the semantic information of maize plants into the feature extraction. The regional growth method segments the maize stalk instances, the instances are parameterized to a line model, and the optimization method calculates the pose transformation. Finally, the mapping method corrects the drift error of the odometry and outputs the maize field map. This paper compares our method with the GICP and LOAM methods. The trajectory relative errors of our method are 0.88%, 0.96%, and 2.12%, respectively, better than other methods. At the same time, the map drawn by our method has less ghosting and clearer plant edges. The results show that our method is more robust and accurate than other methods due to the introduction of semantic information in the environment. The mapping of corn fields can be further used in precision agriculture.
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Yang, Jiannan, Yong Yin, Dengmao Fang, and Fengjiao Zheng. "An Automatic Derivation Method for Creation of Complex Map Symbols in a Topographic Map." ISPRS International Journal of Geo-Information 12, no. 3 (March 1, 2023): 103. http://dx.doi.org/10.3390/ijgi12030103.
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The topographic map plays a very important role in economic construction. In the process of drawing topographic maps, different symbols represent different ground objects, but the symbols representing complex ground objects are often complicated and difficult to create. Moreover, the creation process of complex map symbols can seriously affect the efficiency of topographic map production. Therefore, this paper proposes an automatic derivation method for creation of complex map symbols in a topographic map. The data used are new geographic entity data under the background of Chinese new fundamental surveying and mapping situation. Firstly, four derivation modes of complex map symbols are summarized, including feature-point mode, centroid mode, feature-line mode, and parallel-line mode; then, using the four modes singly or in combination, the complex map symbols of the topographic map are directly derived from the geographic entity data based on programming, and the topographic map cartographic result is obtained automatically. Finally, some topographic maps for Shanxi Province, China, is used for the validation of the creation of map symbols. The experimental results show that the proposed method can automatically derive the complex map symbols of the topographic map, greatly improving production efficiency and obtaining a good visualization effect. The proposed method is a new approach for a new situation and realizes the transformation and upgrading of fundamental surveying and mapping achievements.
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Fang, Baofu, and Zhiqiang Zhan. "A visual SLAM method based on point-line fusion in weak-matching scene." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142090419. http://dx.doi.org/10.1177/1729881420904193.
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Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.
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Luo, J., and Q. Ye. "LIDAR-BASED INITIAL GLOBAL LOCALIZATION USING IMPERFECT ARCHITECTURAL SKELETON INFORMATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 241–48. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-241-2022.
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Abstract. Initial global localization of a mobile robotic platform is the foundation for its navigation and mapping, especially when the platform enters into unknown environments. In GNSS-denied indoor scenes, LiDAR is widely used for robot localization, especially in indoor scenes with poor lighting. In most existing LiDAR-based initial global localization methods, it is necessary to build the point cloud reference map in advance, which costs a large quantity of manpower and time. For this reason, a LiDAR-based initial global localization method using imperfect architectural skeleton information is proposed in this work. Firstly, we propose a lightweight management scheme for collected imperfect architectural information, which is convenient for efficient registration with real scans. Secondly, we extract architectural skeletons (stable man-made structures such as walls and columns) from both architectural information and real scans, and design them as line pairs feature patterns like P-LP, V-LP and C-LP. Thirdly, we propose a matrix descriptor for line pairs feature patterns description and initial matching. Finally, we construct error equations to estimate the pose by initial matching line pairs, and acquire the optimal localization results with the highest hit ratio on architectural grid map. A mobile robotic platform with the 16 beam LiDAR is experimented in typical indoor scenes such as rooms, corridors and undergrounding parking lots. Experiments show that the success rate of initial global localization reaches 80%, the average position error is about 0.10m and the running time is about 400ms per 1000 scans, which meet the requirements of indoor autonomous driving.
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Yu, Zhefu, Paul Martini, A. Penton, T. M. Davis, U. Malik, C. Lidman, B. E. Tucker, et al. "OzDES Reverberation Mapping Programme: the first Mg ii lags from 5 yr of monitoring." Monthly Notices of the Royal Astronomical Society 507, no. 3 (August 5, 2021): 3771–88. http://dx.doi.org/10.1093/mnras/stab2244.
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ABSTRACT Reverberation mapping is a robust method to measure the masses of supermassive black holes outside of the local Universe. Measurements of the radius–luminosity (R−L) relation using the Mg ii emission line are critical for determining these masses near the peak of quasar activity at z ≈ 1−2, and for calibrating secondary mass estimators based on Mg ii that can be applied to large samples with only single-epoch spectroscopy. We present the first nine Mg ii lags from our 5-yr Australian Dark Energy Survey reverberation mapping programme, which substantially improves the number and quality of Mg ii lag measurements. As the Mg ii feature is somewhat blended with iron emission, we model and subtract both the continuum and iron contamination from the multiepoch spectra before analysing the Mg ii line. We also develop a new method of quantifying correlated spectroscopic calibration errors based on our numerous, contemporaneous observations of F-stars. The lag measurements for seven of our nine sources are consistent with both the H β and Mg ii R−L relations reported by previous studies. Our simulations verify the lag reliability of our nine measurements, and we estimate that the median false positive rate of the lag measurements is $4{{\ \rm per\ cent}}$.
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Qin, Jiangying, Ming Li, Xuan Liao, and Jiageng Zhong. "Accumulative Errors Optimization for Visual Odometry of ORB-SLAM2 Based on RGB-D Cameras." ISPRS International Journal of Geo-Information 8, no. 12 (December 11, 2019): 581. http://dx.doi.org/10.3390/ijgi8120581.
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Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry as one of its core components. Given the accumulated error problem with RGB-Depth ORB-SLAM2 visual odometry, which causes a loss of camera tracking and trajectory drift, we created and implemented an improved visual odometry method to optimize the cumulative error. First, this paper proposes an adaptive threshold oFAST algorithm to extract feature points from images and rBRIEF is used to describe the feature points. Then, the fast library for approximate nearest neighbors strategy is used for image rough matching, the results of which are optimized by progressive sample consensus. The image matching precision is further improved by using an epipolar line constraint based on the essential matrix. Finally, the efficient Perspective-n-Point method is used to estimate the camera pose and a least-squares optimization problem is constructed to adjust the estimated value to obtain the final camera pose. The experimental results show that the proposed method has better robustness, higher image matching accuracy and more accurate determination of the camera motion trajectory.
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An, Su-Yong, Lae-Kyoung Lee, and Se-Young Oh. "Line segment-based fast 3D plane extraction using nodding 2D laser rangefinder." Robotica 33, no. 08 (May 1, 2014): 1751–74. http://dx.doi.org/10.1017/s0263574714000927.
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SUMMARYThree-dimensional (3D) data processing has applications in solving complex tasks such as object recognition, environment modeling, and robotic mapping and localization. Because using raw 3D data without preprocessing is very time-consuming, extraction of geometric features that describe the environment concisely is essential. In this sense, a plane can be a suitable geometric feature due to its simplicity of extraction and the abundance in indoor environments. This paper presents an online incremental plane extraction method using line segments for indoor environments. Our data collection system is based on a “nodding” laser scanner, so we exploit the incremental nature of its data acquisition in which physical rotation and 3D data processing are conducted in parallel. Line segments defined by two end points become supporting elements that comprise a plane, so a large proportion of scan points can be ignored once the line segments are extracted from each scan slice. This elimination of points reduces the algorithm complexity and computation time. Experiments with the tens of complete scan data sets which were acquired from a typical indoor environment demonstrated that our method was at least three times faster than the state-of-the-art methods.
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Tu, Xinyuan, Jian Zhang, Runhao Luo, Kai Wang, Qingji Zeng, Yu Zhou, Yao Yu, and Sidan Du. "Reconstruction of High-Precision Semantic Map." Sensors 20, no. 21 (November 3, 2020): 6264. http://dx.doi.org/10.3390/s20216264.
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We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.
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Gu, Liuwan, Hao Zhang, and Xingjie Wu. "SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data." Scientific Programming 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/9562527.
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In order to improve the efficiency of dynamic visualization of large-scale road traffic data in a web environment, this paper proposes a SLAM 3D digital terrain map visualization method using SqueezeNet in a web environment. We propose a hierarchical organization method of the road network, taking into account road attributes and drawing roads at different view heights; a multiroad merging method based on the line segment indexing feature of WebGL (web graphics library) technology, and optimizing the scene by combining view rejection and multithreading technology. The prototype system was developed and case studies were carried out using the national road network data as an example. The experimental results show that the frame rate of large-scale road traffic data visualization in the network environment is above 40 frames/second, which is 20–30 frames/second higher than that of Baidu’s ECharts GL visualization method.
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Rong, Hanxiao, Yanbin Gao, Lianwu Guan, Alex Ramirez-Serrano, Xu Xu, and Yunyu Zhu. "Point-Line Visual Stereo SLAM Using EDlines and PL-BoW." Remote Sensing 13, no. 18 (September 9, 2021): 3591. http://dx.doi.org/10.3390/rs13183591.
Full textAbstract:
Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.
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Bilgic, Hatice, Seungho Cho, David F. Garvin, and Gary J. Muehlbauer. "Mapping barley genes to chromosome arms by transcript profiling of wheat–barley ditelosomic chromosome addition lines." Genome 50, no. 10 (October 2007): 898–906. http://dx.doi.org/10.1139/g07-059.
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Wheat–barley disomic and ditelosomic chromosome addition lines have been used as genetic tools for a range of applications since their development in the 1980s. In the present study, we used the Affymetrix Barley1 GeneChip for comparative transcript analysis of the barley cultivar Betzes, the wheat cultivar Chinese Spring, and Chinese Spring – Betzes ditelosomic chromosome addition lines to physically map barley genes to their respective chromosome arm locations. We mapped 1257 barley genes to chromosome arms 1HS, 2HS, 2HL, 3HS, 3HL, 4HS, 4HL, 5HS, 5HL, 7HS, and 7HL based on their transcript levels in the ditelosomic addition lines. The number of genes assigned to individual chromosome arms ranged from 24 to 197. We validated the physical locations of the genes through comparison with our previous chromosome-based physical mapping, comparative in silico mapping with rice and wheat, and single feature polymorphism (SFP) analysis. We found our physical mapping of barley genes to chromosome arms to be consistent with our previous physical mapping to whole chromosomes. In silico comparative mapping of barley genes assigned to chromosome arms revealed that the average genomic synteny to wheat and rice chromosome arms was 63.2% and 65.5%, respectively. In the 1257 mapped genes, we identified SFPs in 924 genes between the appropriate ditelosomic line and Chinese Spring that supported physical map placements. We also identified a single small rearrangement event between rice chromosome 9 and barley chromosome 4H that accounts for the loss of synteny for several genes.
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Wang, Hongjian, Guixia Fu, Juan Li, Zheping Yan, and Xinqian Bian. "An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/605981.
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This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.
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Wang, Ran, Xin Wang, MingMing Zhu, and YinFu Lin. "Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization." Applied Sciences 9, no. 7 (April 4, 2019): 1428. http://dx.doi.org/10.3390/app9071428.
Full textAbstract:
Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.
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Zhang, R., S. Shi, X. Yi, and M. Jing. "APPLICATION OF RGB-D SLAM IN 3D TUNNEL RECONSTRUCTION BASED ON SUPERPIXEL AIDED FEATURE TRACKING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 559–64. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-559-2022.
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Abstract. In large-scale projects such as hydropower and transportation, the real-time acquisition and generation of the 3D tunnel model can provide an important basis for the analysis and evaluation of the tunnel stability. The Simultaneous Localization And Mapping (SLAM) technology has the advantages of low cost and strong real-time, which can greatly improve the data acquisition efficiency during tunnel excavation. Feature tracking and matching are critical processes of traditional 3D reconstruction technologies such as Structure from Motion (SfM) and SLAM. However, the complicated rock mass structures on the tunnel surface and the limited lighting environment make feature tracking and matching difficult. Manhattan SLAM is a technology integrating superpixels and Manhattan world assumptions, in which both line features and planar features can be better extracted. Rock mass discontinuities including traces and structural planes are distributed on the inner surface of tunnels, which can be extracted for feature tracking and matching. Therefore, this paper proposes a 3D reconstruction pipeline for tunnels, in which, the Manhattan SLAM algorithm is applied for camera pose parameters estimation and the sparse point cloud generation, and the Patch-based Multi-View Stereo (PMVS) is adopted for dense reconstruction. In this paper, the Azure Kinect DK sensor is used for data acquisition. Experiments are proceeded and the results show that the proposed pipeline based on Manhattan SLAM and PMVS performs good robustness and feasibility for tunnels 3D reconstruction.
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Jende, P., M. Peter, M. Gerke, and G. Vosselman. "ADVANCED TIE FEATURE MATCHING FOR THE REGISTRATION OF MOBILE MAPPING IMAGING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 617–23. http://dx.doi.org/10.5194/isprs-archives-xli-b1-617-2016.
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Mobile Mapping’s ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform’s position. Consequently, acquired data products’ positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. <br><br> We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. <br><br> This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images’ geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. <br><br> Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.
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Jende, P., M. Peter, M. Gerke, and G. Vosselman. "ADVANCED TIE FEATURE MATCHING FOR THE REGISTRATION OF MOBILE MAPPING IMAGING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 617–23. http://dx.doi.org/10.5194/isprsarchives-xli-b1-617-2016.
Full textAbstract:
Mobile Mapping’s ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform’s position. Consequently, acquired data products’ positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. <br><br> We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. <br><br> This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images’ geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. <br><br> Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.
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Im, Gyubeom, Minsung Kim, and Jaeheung Park. "Parking Line Based SLAM Approach Using AVM/LiDAR Sensor Fusion for Rapid and Accurate Loop Closing and Parking Space Detection." Sensors 19, no. 21 (November 5, 2019): 4811. http://dx.doi.org/10.3390/s19214811.
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Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should be maintained even when loop closing occurs. This study proposes a simultaneous localization and mapping (SLAM) method, using around view monitor (AVM)/light detection and ranging (LiDAR) sensor fusion, that provides rapid loop closing performance. We extract the parking line features by utilizing the sensor fusion data for sparse feature-based pose graph optimization that boosts the loop closing speed. Hence, the proposed method can perform the loop closing within a few milliseconds to compensate for the accumulative errors even in a large-scale outdoor environment, which is much faster than other LiDAR-based SLAM algorithms. Therefore, it easily satisfies real-time localization performance. Furthermore, thanks to the parking line features, the proposed method can detect a parking space by utilizing the accumulated parking lines in the map. The experiment was performed in three outdoor parking lots to validate the localization performance and parking space detection performance. All of the proposed methods can be operated in real-time in a single-CPU environment.
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Sompolska-Rzechuła, Agnieszka. "Selection of the method of linear ordering using the example of assessing the level of socio-economic development of European Union countries." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu 64, no. 7 (2020): 118–29. http://dx.doi.org/10.15611/pn.2020.7.09.
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The aim of the article is to present the issues of choosing the optimal procedure for the linear ordering of objects and assessing the correctness of the selected methods of the linear ordering. The goal was achieved by creating linear ordering of objects using various methods for normalizing the value of diagnostic features. An aggregate measure based on various properties of the synthetic feature was used to select the optimal ordering, among others, the compatibility of the mapping, the correlation of the synthetic line variable with diagnostic variables, the rank correlation of the synthetic variable with diagnostic variables and the variability of the synthetic variable. The study was conducted based on the example of data concerning 28 European Union countries according to the level of socio-economic development in the context of sustainable development concerning society, economy and the environment. The linear ordering of countries using the quotient transformation with an arithmetic mean turned out to be the most correct ordering
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Wei, Dehua, Xiukun Wei, and Limin Jia. "Automatic Defect Description of Railway Track Line Image Based on Dense Captioning." Sensors 22, no. 17 (August 25, 2022): 6419. http://dx.doi.org/10.3390/s22176419.
Full textAbstract:
The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods.
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Zhang, Xiaoyu, Wei Wang, Xianyu Qi, Ziwei Liao, and Ran Wei. "Point-Plane SLAM Using Supposed Planes for Indoor Environments." Sensors 19, no. 17 (September 2, 2019): 3795. http://dx.doi.org/10.3390/s19173795.
Full textAbstract:
Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.
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Yang, Xin, Xiaohu Lin, Wanqiang Yao, Hongwei Ma, Junliang Zheng, and Bolin Ma. "A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation." Remote Sensing 15, no. 1 (December 29, 2022): 186. http://dx.doi.org/10.3390/rs15010186.
Full textAbstract:
Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection.
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Yu, Qinghao, Hui Yu, Yongxiong Wang, and Tuan D. Pham. "SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External Attention." Electronics 11, no. 21 (October 29, 2022): 3523. http://dx.doi.org/10.3390/electronics11213523.
Full textAbstract:
Video summarization aims to generate a sparse subset that is more concise and less redundant than the original video while containing the most informative parts of the video. However, previous works ignore the prior knowledge of the distribution of interestingness of video frames, making it hard for the network to learn the importance of different frames. Furthermore, traditional models alone (such as RNN and LSTM) are not robust enough in capturing global features of the video sequence since the video frames are more in line with non-Euclidean data structure. To this end, we propose a new summarization method based on the graph model concept to learn the feature relationship connections between video frames, which can guide the summary generator to generate a robust global feature representation. Specifically, we propose to use adversarial learning to integrate Gaussian distribution and external attention mechanism (SUM-GAN-GEA). The Gaussian function is a priori mapping function that considers the distribution of the interestingness of actual video frames and the external attention can reduce the inference time of the model. Experimental results on two popular video abstraction datasets (SumMe and TVSum) demonstrate the high superiority and competitiveness of our method in robustness and fast convergence.
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Zhang, Chi, Zhongze Tang, Min Zhang, Bo Wang, and Lei Hou. "Developing a More Reliable Aerial Photography-Based Method for Acquiring Freeway Traffic Data." Remote Sensing 14, no. 9 (May 5, 2022): 2202. http://dx.doi.org/10.3390/rs14092202.
Full textAbstract:
Due to the widespread use of unmanned aerial vehicles (UAVs) in remote sensing, there are fully developed techniques for extracting vehicle speed and trajectory data from aerial video, using either a traditional method based on optical features or a deep learning method; however, there are few papers that discuss how to solve the issue of video shaking, and existing vehicle data are rarely linked to lane lines. To address the deficiencies in current research, in this study, we formulated a more reliable method for real traffic data acquisition that outperforms the traditional methods in terms of data accuracy and integrity. First, this method implements the scale-invariant feature transform (SIFT) algorithm to detect, describe, and match local features acquired from high-altitude fixed-point aerial photographs. Second, it applies “you only look once” version 5 (YOLOv5) and deep simple online and real-time tracking (DeepSORT) to detect and track moving vehicles. Next, it leverages the developed Python program to acquire data on vehicle speed and distance (to the marked reference line). The results show that this method achieved over 95% accuracy in speed detection and less than 20 cm tolerance in vehicle trajectory mapping. This method also addresses common problems involving the lack of quality aerial photographic data and accuracy in lane line recognition. Finally, this approach can be used to establish a Frenet coordinate system, which can further decipher driving behaviors and road traffic safety.