HP2-SLAM：用于鲁棒高效激光雷达SLAM的自适应混合ICP

中文摘要 AI 辅助

同时实现鲁棒性、精度和效率仍是激光雷达（LiDAR）同步定位与建图（SLAM）领域的核心挑战。尽管基于学习的方法在基准测试中表现出色，但它们通常需要大量训练、充足计算资源，且难以泛化到未知或退化环境。基于几何的方法高效且可解释，但受限于标准迭代最近点（ICP）公式，在平面或重复场景中性能会下降。本文提出HP2-SLAM，这是一个围绕邻域大小自适应混合ICP构建的极简但鲁棒的LiDAR SLAM框架。核心思路是采用平面感知自适应阈值，根据局部几何结构和密度动态分类对应关系，从而在点到平面和点到点残差之间实现合理平衡。该公式无需特征工程、学习模块或特定数据集调优，即可在结构化和退化环境中稳定配准。将其集成到包含子图管理、回环检测和位姿图优化的完整SLAM流水线后，HP2-SLAM在公开数据集上始终优于强大的基于几何的基准方法，同时在商用硬件上保持实时性能。结果表明，精心设计的几何适配可在不牺牲简洁性或效率的前提下实现强泛化性和鲁棒性。

英文摘要

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.

发表机构

• University of Arkansas（阿肯色大学）
• VinRobotics（维恩机器人公司）
• University of Texas at Dallas（德克萨斯大学达拉斯分校）

机构由 AI 辅助整理，请以论文原文为准。