Object SLAM With Robust Quadric Initialization and Mapping for Dynamic Outdoors

Object SLAM is a popular approach for autonomous driving and robotics, but accurate object perception in outdoor environments remains a challenge. State-of-the-art object SLAM algorithms rely on assumptions and are sensitive to observation noise, limiting their application in real-world scenarios. To address these challenges, the authors propose a novel object SLAM system that utilizes a quadric initialization algorithm based on constrained quadric optimization, which does not rely on planar assumptions and is robust to partial observations. Additionally, they introduce an automatic object data association algorithm capable of detecting motion states while associating objects across frames. To further enhance the accuracy of the quadric mapping, an extra thread is used to refine the ellipsoid parameters within a local sliding window composed of keyframes. THeir system utilizes a joint optimization framework that optimizes camera poses, object landmarks, and point clouds in the local mapping thread for further global optimization while maintaining a consistent map. Experimental results on the real-world KITTI dataset show that the proposed system is more robust and significantly outperforms current state-of-the-art methods in quadric initialization and mapping in outdoor scenarios. Moreover, their system achieves real-time performance, making it suitable for practical applications.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3281837
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Tian, Rui
  • Zhang, Yunzhou
  • Cao, Zhenzhong
  • Zhang, Jinpeng
  • Yang, Linghao
  • Coleman, Sonya
  • Kerr, Dermott
  • Li, Kun
• Publication Date: 2023-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 11080-11095
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Cameras; Detection and identification; Mapping
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01907683
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 9 2024 2:51PM