HP2-SLAM: 適応型ハイブリッドICPによる堅牢で効率的なLiDAR SLAM
HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM
平面性を考慮した適応閾値で点対平面と点対点の残差を動的に切り替えるハイブリッドICPを提案し、構造化・退化環境の両方で安定したLiDAR SLAMを実現した。
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著者: Nam Tran, Thu Tran, Hieu Phan, Thai Luu, Toan Nguyen, William J. Beksi, Tuan Dang
分類: cs.RO
原文アブストラクト
Achieving robustness, accuracy, and efficiency simultaneously remains a central challenge in light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM). While learning-based approaches deliver strong benchmark performance, they often require extensive training, substantial computational resources, and struggle to generalize to unseen or degenerate environments. Geometry-based methods are efficient and interpretable, yet their performance degrades in planar or repetitive scenes due to limitations of standard iterative closest point (ICP) formulations. We present HP2-SLAM, a minimalist yet robust LiDAR SLAM framework built around a neighborhood-size adaptive hybrid ICP. Our key insight is a planarity-aware adaptive threshold that dynamically classifies correspondences based on local geometric structure and density, thereby enabling a principled balance between point-to-plane and point-to-point residuals. This formulation stabilizes alignment in both structured and degenerate environments without feature engineering, learning modules, or dataset-specific tuning. Integrated into a complete SLAM pipeline with submap management, loop closure detection, and pose graph optimization, HP2-SLAM consistently outperforms strong geometry-based baselines across publicly available datasets while maintaining real-time performance on commodity hardware. Our results demonstrate that carefully designed geometric adaptation can achieve strong generalization and robustness without sacrificing simplicity or efficiency.