Chuanzhao Lu — accepted AI-conference papers

2024

ATI-CTLO: Adaptive Temporal Interval-Based Continuous-Time LiDAR-Only Odometry

Bo Zhou, Jiajie Wu, Yan Pan, Chuanzhao Lu

RA-L 2024

The motion distortion in LiDAR scans caused by the robot's aggressive motion and environmental terrain features significantly impacts the positioning and mapping performance of 3D LiDAR odometry. Existing distortion correction solutions struggle to balance computational complexity and accuracy. In t

Cited by 3 Source ↗Scholar ↗

2023

MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation With Bird's Eye View Based Appearance and Motion Features

Bo Zhou, Jiapeng Xie, Yan Pan, Jiajie Wu, Chuanzhao Lu

RA-L 2023

Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static map construction. In this letter, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentatio

Cited by 26 Source ↗code ↗Scholar ↗