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Terrestrial lidar scans were captured using a BLK360 scanner (Leica Geosystems, Norcross, GA, USA) which has a range of 0.5 – 45 m and measurement rate up to 680,000 points s−1 at the high-resolution setting. A georeferenced, 3-D point cloud of the study site was generated from 12 scans, approximately 50 m apart in both horizontal directions. Scans were performed in orientations intended to maximize branch exposure to the scanner and to scan during optimal weather conditions to minimize occlusion of features due to noise or movement generated by wind. Scan co-registration was done in Leica Geosystem’s Cyclone Register 360 software using its Visual Simultaneous Localization and Mapping algorithm (Visual SLAM) and resulted in relatively low overall co-registration error ranging from 0.005-0.009 m. From this study site point cloud, manual straight-line measurements from the ground to the sensors were made using Leica’s Cyclone Register 360 software.
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LiDAR Point Cloud Registration with Formal Guarantees
In recent years, LiDAR sensors have become pervasive in the solutions to localization tasks for autonomous systems. One key step in using LiDAR data for localization is the alignment of two LiDAR scans taken from different poses, a process called scan-matching or point cloud registration. Most existing algorithms for this problem are heuristic in nature and local, meaning they may not produce accurate results under poor initialization. Moreover, existing methods give no guarantee on the quality of their output, which can be detrimental for safety-critical tasks. In this paper, we analyze a simple algorithm for point cloud registration, termed PASTA. This algorithm is global and does not rely on point-to-point correspondences, which are typically absent in LiDAR data. Moreover, and to the best of our knowledge, we offer the first point cloud registration algorithm with provable error bounds. Finally, we illustrate the proposed algorithm and error bounds in simulation on a simple trajectory tracking task.
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- PAR ID:
- 10411917
- Date Published:
- Journal Name:
- 2022 IEEE 61st Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 3462 to 3467
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC51059.2022.9992625
