Готові списки джерел за темами / High-Precision Mapping

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Добірка наукової літератури з теми "High-Precision Mapping"

Автор: Grafiati

Опубліковано: 24 лютого 2024

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Статті в журналах з теми "High-Precision Mapping"

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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Wang, Qingshan, Jun Zhang, Yuansheng Liu, and Xinchen Zhang. "High-Precision and Fast LiDAR Odometry and Mapping Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (March 20, 2022): 206–16. http://dx.doi.org/10.20965/jaciii.2022.p0206.

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LiDAR SLAM technology is an important method for the accurate navigation of automatic vehicles and is a prerequisite for the safe driving of automatic vehicles in the unstructured road environment of complex parks. This paper proposes a LiDAR fast point cloud registration algorithm that can realize fast and accurate localization and mapping of automatic vehicle point clouds through a combination of a normal distribution transform (NDT) and point-to-line iterative closest point (PLICP). First, the NDT point cloud registration algorithm is applied for the rough registration of point clouds between adjacent frames to achieve a rough estimate of the pose of automatic vehicles. Then, the PLICP point cloud registration algorithm is adopted to correct the rough registration result of the point cloud. This step completes the precise registration of the point cloud and achieves an accurate estimate of the pose of the automatic vehicle. Finally, cloud registration is accumulated over time, and the point cloud information is continuously updated to construct the point cloud map. Through numerous experiments, we compared the proposed algorithm with PLICP. The average number of iterations of the point cloud registration between adjacent frames was reduced by 6.046. The average running time of the point cloud registration between adjacent frames decreased by 43.05156 ms. The efficiency of the point cloud registration calculation increased by approximately 51.7%. By applying the KITTI dataset, the computational efficiency of NDT-ICP was approximately 60% higher than that of LeGO-LOAM. The proposed method realizes the accurate localization and mapping of automatic vehicles relying on vehicle LiDAR in a complex park environment and was applied to a Small Cyclone automatic vehicle. The results indicate that the proposed algorithm is reliable and effective.

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Liu, Cong, Licheng Wang, Xiaopeng Liu, and Zhihong Xu. "Iterative mapping for high-precision calibration and displacement measurements." Optik 248 (December 2021): 168195. http://dx.doi.org/10.1016/j.ijleo.2021.168195.

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Haas, Benedikt, Candice Thomas, Pierre-Henri Jouneau, Nicolas Bernier, Tristan Meunier, Philippe Ballet, and Jean-Luc Rouvière. "High precision strain mapping of topological insulator HgTe/CdTe." Applied Physics Letters 110, no. 26 (June 26, 2017): 263102. http://dx.doi.org/10.1063/1.4989822.

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Iwakiri, Yuya, and Toyohisa Kaneko. "High-precision texture mapping on 3D free-form objects." Electronics and Communications in Japan (Part II: Electronics) 89, no. 9 (2006): 24–32. http://dx.doi.org/10.1002/ecjb.20302.

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Cruz, Isabel F., Matteo Palmonari, Federico Caimi, and Cosmin Stroe. "Building linked ontologies with high precision using subclass mapping discovery." Artificial Intelligence Review 40, no. 2 (November 9, 2012): 127–45. http://dx.doi.org/10.1007/s10462-012-9363-x.

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Bo, Zheng, Kaichang Di, Bin Liu, Jia Wang, Zhaoqin Liu, Xin Xin, Ziqing Cheng, and Jinkuan Yin. "High-Precision Registration of Lunar Global Mapping Products Based on Spherical Triangular Mesh." Remote Sensing 14, no. 6 (March 16, 2022): 1442. http://dx.doi.org/10.3390/rs14061442.

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Lunar global mapping products provide a solid data foundation for lunar scientific research and exploration. As the widespread geometric inconsistencies among multi-source mapping products seriously affect the synergistic use of the products, high-precision registration of multiple lunar global products is critical, and it is highly challenging due to large coverage and complex local geometric inconsistencies. In this research, we propose a spherical triangular-mesh-based method for high-precision registration of lunar global mapping products, which involves four steps: data preprocessing, feature point extraction and matching, spherical Delaunay triangulation, and geometric correction with spherical barycentric coordinates. This global registration method avoids map projection distortions by using spherical coordinates directly, and achieves high precision by confining the geometric models to spherical triangular facets. Experiments are conducted using two groups of different types of mapping products to verify the proposed method quantitatively and qualitatively. The results show that the geometric inconsistencies are reduced from hundreds of pixels to sub-pixel level globally after the registration, demonstrating the effectiveness of the proposed method.

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Mostafa, M. M. R. "ACCURACY ASSESSMENT OF PROFESSIONAL GRADE UNMANNED SYSTEMS FOR HIGH PRECISION AIRBORNE MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 24, 2017): 257–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-257-2017.

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Recently, sophisticated multi-sensor systems have been implemented on-board modern Unmanned Aerial Systems. This allows for producing a variety of mapping products for different mapping applications. The resulting accuracies match the traditional well engineered manned systems. This paper presents the results of a geometric accuracy assessment project for unmanned systems equipped with multi-sensor systems for direct georeferencing purposes. There are a number of parameters that either individually or collectively affect the quality and accuracy of a final airborne mapping product. This paper focuses on identifying and explaining these parameters and their mutual interaction and correlation. Accuracy Assessment of the final ground object positioning accuracy is presented through real-world 8 flight missions that were flown in Quebec, Canada. The achievable precision of map production is addressed in some detail.

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Liu, Y., B. Liu, B. Xu, Z. Liu, K. Di, and J. Zhou. "High Precision Topographic Mapping at Chang'E-3 Landing Site with Multi-Source Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (April 23, 2014): 157–61. http://dx.doi.org/10.5194/isprsarchives-xl-4-157-2014.

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Chang'e-3 (CE-3) is the first lander and rover of China following the success of Chang'e-1 and Chang'e-2 (CE-2) orbiters. High precision topographic mapping can provide detailed terrain information to ensure the safety of the rover as well as to support scientific investigations. In this research, multi-source data are co-registered into a uniform geographic framework for high precision topographic mapping at the CE-3 landing site. CE-2 CCD images with 7 m- and 1.5 m- resolutions are registered using selfcalibration bundle adjustment method with ground control points (GCPs) selected from LRO WAC mosaic map and LOLA DTM. The trajectory of CE-3 descent images are recovered using self-calibration free net bundle adjustment, and then the topographic data is rectified by absolute orientation with GCPs selected from the adjusted CE-2 DEM and DOM. Finally, these topographic data are integrated into the same geographic framework for unified, multi-scale, high precision mapping of the CE-3 landing site. Key technologies and the mapping products of this research have been used to support the surface operations of CE-3 mission.

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Yoon, Dasol, Harikrishnan K.P., Yu-Tsun Shao, and David A. Muller. "High-Speed, High-Precision, and High-Throughput Strain Mapping with Cepstral Transformed 4D-STEM Data." Microscopy and Microanalysis 28, S1 (July 22, 2022): 796–98. http://dx.doi.org/10.1017/s1431927622003592.

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Більше джерел

Дисертації з теми "High-Precision Mapping"

Stoven-Dubois, Alexis. "Robust Crowdsourced Mapping for Landmarks-based Vehicle Localization." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2022. http://www.theses.fr/2022UCFAC116.

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Le déploiement de véhicules intelligents et connectés, dotés de capteurs de plus en plus sophistiqués, et capables de partager des positions et des trajectoires précises, permettra d’améliorer considérablement la sécurité routière et l’efficacité du trafic. Pour que ce gain de sécurité devienne effectif, les véhicules devront être géo-positionnés dans un référentiel commun avec précision, avec une erreur d’au plus quelques décimètres [1]. Pour y parvenir, ils pourront compter sur une variété de capteurs embarqués, tels que des récepteurs GNSS (Global Navigation Satellite Systems), ainsi que des capteurs proprioceptifs et des capteurs de perception. Toutefois, afin de garantir un positionnement précis dans toutes les conditions, y compris dans les zones denses où les signaux GNSS peuvent être dégradés par des effets de trajets multiples, les véhicules devront utiliser des cartes précises de l’environnement pour soutenir leurs algorithmes de localisation.Afin d’établir de telles cartes pour les principales autoroutes, les principaux acteurs automobiles ont eu recours à des flottes de véhicules spécialisés équipés de capteurs haut de gamme. Cependant, en raison des coûts opérationnels élevés qui y sont associés, ils n’ont exploité qu’un nombre limité de véhicules et ne sont pas en mesure de fournir des mises à jour en direct des cartes, ni de cartographier des réseaux routiers entiers. La cartographie crowdsourcée représente une solution rentable à ce problème et suscite aujourd’hui l’intérêt des acteurs du secteur automobile. Cette technique consiste à exploiter les mesures récupérées par de multiples véhicules de production équipés de capteurs standard, afin de construire une carte contenant des points de repère. Néanmoins, même si cette approche semble prometteuse, sa capacité réelle à construire une carte précise et à la maintenir à jour a besoin d’être évaluée dans des scénarios réalistes et long-terme.Dans cette thèse, nous proposons d’abord une solution de cartographie crowdsourcée basée sur une optimisation par triangulation, et l’évaluons à l’aide de tests de terrain. L’analyse des résultats montre le potentiel de cette approche à tirer profit des mesures émises par plusieurs véhicules. Elle permet aussi d’identifier certaines limitations critiques associées à l’optimisation par triangulation.Pour remédier à cela, nous proposons ensuite une autre solution de cartographie crowdsourcée basée sur l’optimisation de graphe, et nous introduisons différentes approches pour inclure et mettre à jour la carte dans l’optimisation, qui correspondent à différents compromis entre la qualité de la carte et la scalabilité. Des expériences de simulation sont menées afin de comparer ces approches. Les résultats permettent d’identifier la plus efficace, ainsi que de vérifier qu’elle représente une solution scalable de cartographie crowdsourcée.La robustesse de cette approche à divers types de bruits, tels que les bruits auto-corrélés et biaisés, est ensuite évaluée à l’aide de tests de simulation étendus. L’analyse des résultats montre sa capacité à construire une carte précise dans diverses conditions de bruits, en utilisant des mesures récupérées par plusieurs véhicules. Ensuite, des tests de terrain sont effectués afin de confirmer les résultats obtenus en simulation, et de tirer des conclusions tant d’un point de vue théorique que pratique. Enfin, la capacité de notre solution de cartographie crowdsourcée à améliorer les capacités de localisation des véhicules est évaluée en simulation. Les résultats montrent l’efficacité de l’approche proposée dans diverses conditions, tout en soulignant l’importance de fournir une carte avec une densité suffisante de points de repère
The deployment of intelligent and connected vehicles, equipped with increasingly sophisticated equipment, and capable of sharing accurate positions and trajectories, is expected to lead to a substantial improvement of road safety and traffic efficiency. For this safety gain to become effective, vehicles will have to be accurately geo-positioned in a common reference, with an error up to a few decimeters [1]. To achieve this, they will be able to count on a variety of embedded sensors, such as GNSS (Global Navigation Satellite Systems) receivers, as well as additional proprioceptive and perception sensors. Nevertheless, in order to guarantee accurate positioning in all conditions, including in dense zones where GNSS signals can get degraded by multi-path effects, it is expected that vehicles will need to use precise maps of the environment to support their localization algorithms.To build maps of the main highways, major automotive actors have made use of dedicated fleets of vehicles equipped with high-end sensors. Because of the associated high operational costs, they have been operating a limited number of vehicles, and remain unable to provide live updates of the maps and to register entire road networks. Crowdsourced mapping represents a cost-effective solution to this problem, and has been creating interest among automotive players. It consists in making use of measurements retrieved by multiple production vehicles equipped with standard sensors in order to build a map of landmarks. Nevertheless, while this approach appears promising, its real potential to build an accurate map of landmarks and maintain it up-to-date remains to be assessed in realistic, long-term scenarios.In this thesis, in a first time, we propose a crowdsourced mapping solution based on triangulation optimization, and evaluate it using field-tests. The result analysis shows the potential of crowdsourced mapping to take advantage from measurements issued by multiple vehicles. On the other hand, it also indicates some critical limitations associated with triangulation optimization.Therefore, in a second time, we propose another crowdsourced mapping solution based on graph optimization, and we introduce different approaches to include and update the map within the optimization, which correspond to different trade-offs between the map quality and computational scalability. Simulation experiments are conducted in order to compare the different approaches. The results enable to identify the most efficient one, and to assert that it provides a scalable solution for crowdsourced mapping.The robustness of this solution to various types of noises, such as auto-correlated and biased noises, is then evaluated using extended simulation tests. The results analysis show its ability to build an accurate map of landmarks in various noises conditions, making use of measurements retrieved by multiple vehicles. Subsequently, field-tests are performed to confirm the results obtained in simulation, and draw conclusions both from a theoretical and practical viewpoint. Finally, the capacity of our crowdsourced mapping solution to increase the localization capabilities of vehicles is evaluated in simulation. The results show the effectiveness of the proposed approach to improve positioning performances in various conditions, while also pointing out the importance of providing a map with a sufficient density of landmarks

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Colley, Richard T. III. "Development of a Machine Vision System for Mass Flow Sensing and High-Resolution Mapping of Granular Fertilizer Application." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543564969065918.

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Pelcat, Yann S. "Soil landscape characterization of crop stubble covered fields using Ikonos high resolution panchromatic images." Thesis, Winnipeg : University of Manitoba, 2006. http://www.collectionscanada.ca/obj/s4/f2/dsk3/MWU/TC-MWU-224.pdf.

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Thesis (M.Sc.)--University of Manitoba, 2006.
A thesis submitted to the Faculty of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science, Department of Soil Science. Includes bibliographical references.

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Книги з теми "High-Precision Mapping"

Michel, Christoph M., and Bin He. EEG Mapping and Source Imaging. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0045.

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This chapter describes methods to analyze the scalp electric field recorded with multichannel electroencephalography (EEG). With advances in high-density EEG, systems now allow fast and easy recording from 64 to 256 channels simultaneously. Pattern-recognition algorithms can characterize the topography of scalp electric fields and detect changes in topography over time and between experimental or clinical conditions. Methods for estimating the sources underlying the recorded scalp potential maps have increased the spatial resolution of EEG. The use of anatomical information in EEG source reconstruction has increased the precision of EEG source localization. Algorithms of functional connectivity applied to the source space allow determination of communication between large-scale brain networks in certain frequencies and identification of the directionality of this information flow and detection of crucial drivers in these networks. These methods have boosted the use of EEG as a functional neuroimaging method in experimental and clinical applications.

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Частини книг з теми "High-Precision Mapping"

Hanning, Tobias. "Modelling the camera mapping." In High Precision Camera Calibration, 5–26. Wiesbaden: Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-9830-2_2.

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Griffiths, Hugh. "Advances in Radar Altimetry Techniques for Topographic Mapping." In High Precision Navigation, 251–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74585-0_18.

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Wehr, Aloysius. "3D-Mapping by a Semiconductor Laser Scanner, Description of an Experimental Setup." In High Precision Navigation, 469–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74585-0_35.

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Hwang, C. S., W. C. Chou, J. H. Huang, M. Y. Lin, Tzuchu Chang, and P. K. Tseng. "High Precision Automatic Magnetic Field Mapping System for the Dipole Magnet." In 11th International Conference on Magnet Technology (MT-11), 291–96. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0769-0_50.

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Tu, Zhiming, Hao Fu, and Zhenping Sun. "LiDAR-Based High-Precision Mapping and GNSS-Denied Localiztion for UAV." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022), 2977–87. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_275.

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Guo, Peng, Qingshan Wang, Zhan Cao, and Haipeng Xia. "High Precision Odometer and Mapping Algorithm Based on Multi Lidar Fusion." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022), 40–50. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_5.

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Li, Jindong. "Design and Analysis of High-Precision Stereo Surveying and Mapping Satellite System." In Space Science and Technologies, 227–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4871-0_6.

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Bordatchev, E. V. "Analysis and Mapping of the Dynamic Performance of High-Precision Motion Systems." In Integrated Design and Manufacturing in Mechanical Engineering, 255–62. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9966-5_30.

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Jing, Huang, Amit Yadav, Asif Khan, and Dakshina Yadav. "A High-Precision Pixel Mapping Method for Image-Sensitive Areas Based on SVR." In Advances in Intelligent Systems and Computing, 35–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6584-7_4.

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Wan, Wenhui, Zhaoqin Liu, and Kaichang Di. "A New Method for Real-Time High-Precision Planetary Rover Localization and Topographic Mapping." In Communications in Computer and Information Science, 215–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37149-3_26.

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Тези доповідей конференцій з теми "High-Precision Mapping"

Doerry, Armin W. "High-precision stereoscopic 3D mapping accuracy." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Edmund G. Zelnio. SPIE, 2001. http://dx.doi.org/10.1117/12.438240.

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Sonneland, L. "High precision fluid mapping in compacting reservoirs." In EAGE/SEG Research Workshop on Reservoir Rocks - Understanding reservoir rock and fluid property distributions - measurement, modelling and applications. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609.201406713.

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Gordon, Jonathan, Jerry Hobbs, Jonathan May, and Fabrizio Morbini. "High-Precision Abductive Mapping of Multilingual Metaphors." In Proceedings of the Third Workshop on Metaphor in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/w15-1406.

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Prexl, Jonathan, Sudipan Saha, and Michael Schmitt. "High Precision Mapping Of Building Changes Using Sentinel-2." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10283173.

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Wang, Fei, and Miaole Hou. "Virtual restoration of Buddha statues based on high-precision 3D models." In Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), edited by Tarun Kumar Lohani. SPIE, 2023. http://dx.doi.org/10.1117/12.2668118.

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Yang, Yue, Zhuqing Yuan, Shuangcai Liu, Wenyu Sun, Yongpan Liu, and Sheng Zhang. "Deep compression for real-time high-precision SAR image ship detection." In 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), edited by Yi Wang and Tao Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3021068.

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wan, haoming, panpan tang, shi bai, and xiaoyan luo. "High-precision mapping of smallholder rapeseed combining UAV imagery and deep learning." In 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), edited by Yi Wang and Tao Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3021008.

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Ding, Shulei, Zhuolu Hou, Jiaxun Jiang, Li Zhang, and Yuxuan Liu. "High-Precision Geometric Positioning of Optical Satellite Images Assisted by LiDAR Data." In 2023 5th International Conference on Geoscience and Remote Sensing Mapping (GRSM). IEEE, 2023. http://dx.doi.org/10.1109/grsm60169.2023.10425585.

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Blank, Sebastian, Yantao Shen, Ning Xi, Chi Zhang, and Uchechukwu C. Wejinya. "High precision PSD guided robot localization: Design, mapping, and position control." In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iros.2007.4399621.

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He, Chunjing, Angze Li, Lirong Qiu, and Weiqian Zhao. "Three-dimensional high-precision mineral mapping using confocal controlled LIBS microscope." In Advanced Optical Imaging Technologies V, edited by P. Scott Carney, Xiao-Cong Yuan, and Kebin Shi. SPIE, 2023. http://dx.doi.org/10.1117/12.2655745.

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Звіти організацій з теми "High-Precision Mapping"

Gardner, J. N., A. Lavine, D. Vaniman, and G. WoldeGabriel. High-precision geologic mapping to evaluate the potential for seismic surface rupture at TA-55, Los Alamos National Laboratory. Office of Scientific and Technical Information (OSTI), June 1998. http://dx.doi.org/10.2172/661496.

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.

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Dudley, J. P., and S. V. Samsonov. SAR interferometry with the RADARSAT Constellation Mission. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329396.

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The RADARSAT Constellation Mission (RCM) is Canada's latest system of C-band Synthetic Aperture Radar (SAR) Earth observation satellites. The system of three satellites, spaced equally in a common orbit, allows for a rapid four-day repeat interval. The RCM has been designed with a selection of stripmap, spotlight, and ScanSAR beam modes which offer varied combinations of spatial resolution and coverage. Using Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, the growing archive of SAR data gathered by RCM can be used for change detection and ground deformation monitoring for diverse applications in Canada and around the world. In partnership with the Canadian Space Agency (CSA), the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products and change detection from SAR data acquired by RCM and RADARSAT-2 satellites using DInSAR processing methodology. Using this system, this paper investigates four key interferometric properties of the RCM system which were not available on the RADARSAT-1 or RADARSAT-2 missions: The impact of the high temporal resolution of the four-day repeat cycle of the RCM on temporal decorrelation trends is tested and fitted against simple temporal decay models. The effect of the normalization and the precision of the radiometric calibration on interferometric spatial coherence is investigated. The performance of the RCM ScanSAR mode for wide area interferometric analysis is tested. The performance of the novel RCM Compact-polarization (CP) mode for interferometric analysis is also investigated.

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Schmidt, Elizabeth. Shoreline change at Fort Matanzas National Monument: 2020–2021 data summary. National Park Service, January 2022. http://dx.doi.org/10.36967/nrds-2290193.

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In 2020 and 2021 the Southeast Coast Network (SECN) collected shoreline data at Fort Matanzas National Monument as a part of the NPS Vital Signs Monitoring Program. Monitoring was conducted following methods developed by the National Park Service Northeast Barrier Coast Network and consisted of mapping the high tide swash line using a global positioning system (GPS) unit in the spring of each year (Psuty et al. 2010). Shoreline change was calculated using the Digital Shoreline Analysis System (DSAS) developed by USGS (Theiler et al. 2008). Key findings from this effort: A mean of 2,255.23 meters (7,399 feet [ft]) of shoreline were mapped from 2020 to 2021 with a mean horizontal precision of 10.73 centimeters (4.2 inches [in]) at Fort Matanzas National Monument from 2020 to 2021. In the annual shoreline change analysis, the mean shoreline distance change from spring 2020 to spring 2021 was -7.40 meters (-24.3 ft) with a standard deviation of 20.24 meters (66.40 ft). The shoreline change distance ranged from -124.73 to 35.59 meters (-409.1 to 116.7 ft). Two erosion areas and one accretion area were identified in the study area beyond the uncertainty of the data (± 10 meters [32.8 ft]). The annual shoreline change from 2020 to 2021 showed erosion on the east and west sides of A1A where the Matanzas Inlet is located. Overall, the most dynamic area of shoreline change within Fort Matanzas National Monument appeared to be on the east and west side of A1A, along the Matanzas River inlet.

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