2026 MRSD Team E Wiki Entry: LIO-SAM with Velocity Undistortion & Dynamic Obstacle Filtering#252
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JoshuaTsai0520 wants to merge 2 commits intoRoboticsKnowledgebase:masterfrom
Open
2026 MRSD Team E Wiki Entry: LIO-SAM with Velocity Undistortion & Dynamic Obstacle Filtering#252JoshuaTsai0520 wants to merge 2 commits intoRoboticsKnowledgebase:masterfrom
JoshuaTsai0520 wants to merge 2 commits intoRoboticsKnowledgebase:masterfrom
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This pull request adds a new state estimation resource to the robotics wiki, focused on improving LIO-SAM mapping quality through velocity-based motion undistortion and dynamic obstacle filtering. The main changes include a practical guide explaining LiDAR motion distortion, 6-DOF distortion correction using vehicle speed information, post-mapping dynamic obstacle removal, and future improvements for more robust map generation.
New LIO-SAM Mapping Resource:
Added a new page, lio-sam-velocity-undistortion-and-dynamic-filtering.md, providing a practical guide to LIO-SAM mapping improvements. The article introduces LIO-SAM, explains LiDAR motion distortion, describes how vehicle speed information can be used for 6-DOF point cloud undistortion, and presents mapping results before and after distortion correction.
Dynamic Obstacle Filtering Workflow:
Included a post-mapping dynamic obstacle filtering workflow using CloudCompare. The guide explains how to manually segment and remove dynamic obstacles and outliers from a LIO-SAM-generated point cloud map, then merge static map segments into a cleaner final map.
Documentation and Navigation Updates:
Added “LIO-SAM with Velocity Undistortion & Dynamic Obstacle Filtering” to the state estimation section of the wiki navigation (_data/navigation.yml).
Included the new LIO-SAM guide in the list of state estimation resources on wiki/state-estimation/index.md, with a short summary of its contents.