# HP2-SLAM：用于鲁棒高效 LiDAR SLAM 的自适应混合 ICP

> 原文链接：https://robotics.hellobike.com/papers/2026-08-15-2608-14996
> 频道：papers | 更新日期：2026-08-15 | 标签：arxiv, cs.RO
> 核心摘要：HP2-SLAM以邻域大小自适应混合ICP提升LiDAR SLAM鲁棒性与效率，并在公开数据集上验证。

# HP2-SLAM：用于鲁棒高效 LiDAR SLAM 的自适应混合 ICP

::: v-pre
> 原文链接：[HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM](https://arxiv.org/abs/2608.14996)（arXiv 2608.14996 · 发布日期：2026-08-15）

**HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM**

HP2-SLAM以邻域大小自适应混合ICP提升LiDAR SLAM鲁棒性与效率，并在公开数据集上验证。

## 核心要点

- LiDAR SLAM难以同时兼顾鲁棒性、精度与效率，传统ICP在平面或重复场景中易退化。
- 方法采用平面性感知的自适应阈值，按局部几何结构与密度动态分类对应关系。
- 框架结合子地图管理、回环检测与位姿图优化，无需特征工程、学习模块或数据集调参。
- 在公开数据集上持续优于强几何基线，并在通用硬件上保持实时性能。

**验证范围与局限**：验证范围为公开可用数据集上的LiDAR SLAM，摘要未说明具体数据集或平台。

## 英文摘要 (Abstract)

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.

作者：Nam Tran, Thu Van Tran, Hieu Phan, Thai Van Luu, Toan Nguyen, William J. Beksi, Tuan Dang
:::

## 原始参考来源 (Sources)

- [arXiv 2608.14996：HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM](https://arxiv.org/abs/2608.14996)