| Author: | Wang, Yibo |
| Title: | Moving-object tracking and removal for point-cloud mapping using a 3-D MEMS LiDAR |
| Advisors: | Sun, Yuxiang (ME) |
| Degree: | M.Sc. |
| Year: | 2023 |
| Department: | Department of Mechanical Engineering |
| Pages: | 59 pages : color illustrations |
| Language: | English |
| Abstract: | Simultaneous Localization and Mapping (SLAM) is an essential technique for mobile robot path planning and navigation. Presently, the majority of LiDAR SLAM systems are typically based on the assumption that the robot is traversing a static environment. However, when the robot moves in a dynamic environment including moving pedestrians or vehicles, the accuracy of its localization is seriously affected by these dynamic objects. Furthermore, unwanted traces of dynamic objects would be remained when accumulating scan data to construct a point cloud map. The unreliable localization and mapping result cannot be utilized for downstream robot tasks such as navigation or reconstruction. Consequently, this dissertation proposes a moving object tracking and removal module which includes ground point cloud filter, clustering based on range image projection, dynamic object recognition and rejection. The proposed module has been integrated into the tightly-coupled LiDAR-Inertial SLAM system, LIO-SAM. The efficacy of this proposed module is evaluated by using our own datasets collected at The Hong Kong Polytechnic University. Experimental results conducted in high dynamic environments demonstrate that the proposed module can effectively filter point cloud of moving objects and retain the static objects during the mapping process of LIO-SAM. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7480.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5 MB | Adobe PDF | View/Open |
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