REGRACE: 一貫性評価を用いた堅牢で効率的なグラフベース再位置推定アルゴリズム
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
LiDARサブマップとグラフニューラルネットワークを用いて、回転不変な物体特徴と近傍文脈を学習し、スケーラブルなBag-of-Wordsで再訪を検出する再位置推定手法を提案。幾何的一貫性で再訪を判定し、従来手法と同等の精度で2倍高速化を実現。
著者: Débora N. P. Oliveira, Joshua Knights, Sebastián Barbas Laina, Simon Boche, Wolfram Burgard, Stefan Leutenegger
分類: cs.CV, cs.RO
原文アブストラクト
Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons. Alternative object-centric approaches are more efficient but often struggle with sensitivity to viewpoint variation. In this work, we introduce REGRACE, a novel approach that addresses these challenges of scalability and perspective difference in re-localization by using LiDAR-based submaps. We introduce rotation-invariant features for each labeled object and enhance them with neighborhood context through a graph neural network. To identify potential revisits, we employ a scalable bag-of-words approach, pooling one learned global feature per submap. Additionally, we define a revisit with geometrical consistency cues rather than embedding distance, allowing us to recognize far-away loop closures. Our evaluations demonstrate that REGRACE achieves similar results compared to state-of-the-art place recognition and registration baselines while being twice as fast. Code and models are publicly available.
