{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T15:47:28Z","timestamp":1764604048913,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Major Project of High Resolution Earth Observation System (Civil Part)","award":["06-Y30F04-9001-20\/22"],"award-info":[{"award-number":["06-Y30F04-9001-20\/22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush\u2013Kuhn\u2013Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results\u2019 sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area\u2019s, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness.<\/jats:p>","DOI":"10.3390\/rs16091620","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T03:30:49Z","timestamp":1714534249000},"page":"1620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Mars Local Terrain Matching Method Based on 3D Point Clouds"],"prefix":"10.3390","volume":"16","author":[{"given":"Binliang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"State Key Lab of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Shuangming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Xinyi","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Institute of Surveying and Mapping, Beijing 100038, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China"},{"name":"Beijing Satellite Application Technology Center, Beijing 100038, China"}]},{"given":"Guorong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","first-page":"28","article-title":"Review of Mars Topographic Mapping","volume":"1","author":"Qing","year":"2014","journal-title":"J. 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