Asynchronous State Estimation of Simultaneous Ego-motion Estimation and Multiple Object Tracking for LiDAR-Inertial Odometry

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose LiDAR-Inertial Odometry via Simultaneous EGo-motion estimation and Multiple Object Tracking (LIO-SEGMOT), an optimization-based odometry approach targeted for dynamic environments. LIO-SEGMOT is formulated as a state estimation approach with asynchronous state update of the odometry and the object tracking. That is, LIO-SEGMOT can provide continuous object tracking results while preserving the keyframe selection mechanism in the odometry system. Meanwhile, a hierarchical criterion is designed to properly couple odometry and object tracking, preventing system instability due to poor detections. We compare LIO-SEGMOT against the baseline model LIO-SAM, a state-of-the-art LIO approach, under dynamic environments of the KITTI raw dataset and the self-collected Hsinchu dataset. The former experiment shows that LIO-SEGMOT obtains an average improvement 1.61% and 5.41% of odometry accuracy in terms of absolute translational and rotational trajectory errors. The latter experiment also indicates that LIO-SEGMOT obtains an average improvement 6.97% and 4.21% of odometry accuracy.

Original languageEnglish
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10616-10622
Number of pages7
ISBN (Electronic)9798350323658
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23

Keywords

  • Autonomous driving
  • multiple object tracking
  • odometry
  • SLAM

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