Pseudo-Anchors: Robust Semantic Features for Lidar Mapping in Highly Dynamic Scenarios

Dynamic environments are challenging for anchor-free mapping using lidar in intelligent driving. This study imitates anchor-based approaches such as magnetic nails by applying novel Static Confidence Criteria (SCC) to the point-cloud semantic candidates to ensure their robustness. The authors name such verified features Pseudo-Anchors (P-A) as they hold similar properties to the anchor nodes: The P-A nodes are improbably formed by dynamic objects, and nodes’ blockage state can be immediately noticed once they are occluded. Another major challenge for mapping is improving large-scale global performance without sacrificing local consistency. Unrecognized GNSS pose drift may deteriorate local trajectory accuracy through post-processing such as graph optimization. In this study, they use the road network to provide the intersection information as a prior so that the GNSS can be better regarded as a reliable anchor factor. Three experiments are designed for this study. The first is ablations to verify the P-A concept; The second proves that the P-A-based lidar odometry outperformed the LOAM-based mainstream methods in highly dynamic scenarios; The third shows that their usage of the GNSS strengthens large-scale maps’ global consistency while causing less deterioration towards the local one. As a knowledge-based method, the P-A concept shows a high deployment efficiency, indicating the potential for migration to other features or even other sensors.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3223219
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Yang, Chenxi
  • He, Lei
  • Zhuang, Hanyang
  • Wang, Chunxiang
  • Yang, Ming
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1619-1630
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 2
  • 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: Automated vehicle control; Intelligent vehicles; Laser radar; Mapping
• Identifier Terms: Global Navigation Satellite System
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01886671
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2023 4:57PM