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Detail
Journal Article
Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot
- Authors
- Ki-In Na, Byungjae Park
- Issue Date
- 2023-10
- Citation
- ETRI Journal, v.45, no.5, pp.836-846
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.
- KSP Keywords
- 3D LIDAR, 3D point cloud, Computational Speed, Convolutional auto-encoder, Integrated system, LiDAR point cloud, Light detection and Ranging(LiDAR), Mobile robots, Object Recognition, Pedestrian Tracking, Pedestrian movement
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
