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.


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