TNVD-SC: Diagnosing Scan Context Failures in Forests with Terrain-Normalized Vertical Distributions

Key points

• We diagnose Scan Context (SC) by separating candidate retrieval from fine matching: on the standardized TreeLoc++ Wild-Places protocol, a TNVD support key places a valid location in the top-25 set for approximately 99% of queries, while conventional SC matching on those candidates reaches only 61.84% mean R@1.1
• With an identical raw-SC candidate set, terrain normalization improves max-height cells by only 1.21 points but vertical-distribution cells by 19.72 points.1
• These observations motivate Terrain-Normalized Vertical Distribution Scan Context (TNVD-SC), which preserves the SC ring-sector topology while encoding terrain-relative vertical probability distributions.1
• A lossless count-coded implementation with exact hierarchical Jensen-Shannon search reduces descriptor payload by 3.86x and compute latency by 23.8% without changing the audited retrieval outputs.1

Sources (1)

• [1]TNVD-SC: Diagnosing Scan Context Failures in Forests with Terrain-Normalized Vertical Distributions
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:28 AM

  Long-term LiDAR place recognition in forests is difficult because repetitive vegetation and uneven terrain create severe structural aliasing.

  We diagnose Scan Context (SC) by separating candidate retrieval from fine matching: on the standardized TreeLoc++ Wild-Places protocol, a TNVD support key places a valid location in the top-25 set for approximately 99% of queries, while conventional SC matching on those candidates reaches only 61.84% mean R@1.

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