日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

週刊ニュースレター購読
地理参照/ポーズグラフ最適化arXiv:2608.16281v1

マーカー制約付きポーズグラフ補正によるGNSS非依存環境でのクロスプラットフォーム地理参照

Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

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GNSSが使えない環境で、カモフラージュ対応のマーカーを視覚アンカーとして用い、LiDARオドメトリとRTAB-Mapの再構成を共通座標系に地理参照するフレームワークを提案。マーカー制約付きポーズグラフ最適化によりドリフトを大幅に低減した。

著者: Marco Giberna, Jose Luis Sanchez Lopez, Holger Voos

分類: cs.RO, cs.MA

原文アブストラクト

Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper presents a framework using camouflage-matched fiducial markers fabricated from Cholesteric Spherical Reflectors (CSRs) as pre-surveyed visual anchors. The anchors georeference both a lightweight LiDAR-odometry trajectory and a dense RTAB-Map reconstruction, allowing their outputs to be expressed in a common LUREF frame (geodetic coordinate reference system used in Luxembourg) without requiring GNSS measurements during operation. The method combines coarse similarity alignment with marker-constrained pose-graph optimization. We evaluate it using two handheld acquisition sessions with ground-level and elevated motion profiles emulating UGV and UAV operation. A single iMarker was relocated among six surveyed positions, with the first position revisited to quantify drift correction. Marker-anchor correction reduced revisit inconsistency by 97.9% and 99.1% for the UAV- and UGV-emulating sessions, respectively, and improved held-out anchor prediction compared with one-time alignment. Separately georeferenced dense reconstructions achieved a median cross-session nearest-neighbour distance of 58 cm without explicit cross-session registration. Marker processing operated in real time, while trajectory correction required less than 0.25 s per session. These results demonstrate a proof of concept for georeferencing lightweight odometry and dense reconstructions using visually unobtrusive, pre-surveyed anchors during GNSS-denied operation.