论文链接：https://ieeexplore.ieee.org/document/11024226

视频链接：https://b23.tv/1qsIj48

开源代码：https://github.com/ghm0819/ERPoT

ERPoT开源：轻量高效，精准可靠，复杂环境定位无忧！

精准定位与鲁棒导航作为移动机器人自主能力发展的基石，在机器人复杂环境应用不断拓展的当下，对高精度、高可靠定位系统的需求变得日益迫切。为满足这一需求，众多先进方法应运而生，用于获取环境地图，为移动机器人在诸如服务、导航、探索等多种应用场景中的定位提供关键的先验信息。当已知先验地图和初始位姿时，定位问题就转化为位姿跟踪问题。然而，现有的位姿跟踪技术存在明显局限性：随着环境规模的扩大，先验地图的数据量呈爆炸式增长，这不仅对存储资源提出了更高要求，还可能大幅降低计算效率，影响位姿跟踪的实时性。此外，许多依赖特定语义特征（如建筑物边缘、道路标志线等）的位姿跟踪方法，在面对缺乏这些语义元素的环境时，往往会出现性能不稳定甚至完全失效的情况。

为了满足移动机器人在各种复杂环境下的应用需求，迫切需要一种能够兼顾地图轻量化与位姿跟踪可靠性的创新方法。本文提出的ERPoT位姿跟踪新方法，因其通用性强，不受特定语义元素限制，故适用于绝大多数任务场景。该方法具有两大创新要点：其一，利用多边形构建先验地图，相较于传统点云地图，不仅大幅压缩了数据量，还保留了关键环境特征，成功实现了大规模环境的轻量化、紧凑表示，有效解决了地图尺寸与跟踪性能之间的矛盾，且位姿跟踪性能表现出色；其二，提出了一种基于 “点-多边形” 匹配的位姿跟踪方法，通过将密集的3D点云数据处理成稀疏的2D扫描数据，降低了数据处理的复杂度，进而构建了一种新型代价函数，巧妙地融合了 “点-顶点”和 “点-边”两种匹配约束形式，从而能够精准、可靠地估计位姿，提升了跟踪的精度和鲁棒性。最终，研究团队在多个涵盖不同典型环境、不同平台及传感器数据、以及存在地形变化的数据集上进行了对比实验，实验结果显示，ERPoT在先验地图大小、位姿估计误差、运行时间等关键指标上，相较于其它六种现有方法均展现出显著优势，充分验证了其卓越的通用性和适应性。

H. Gao, Q. Qiu, H. Liu, D. Liang^*, C. Wang, and X. Zhang, ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using Lightweight Polygon Maps. IEEE Transactions on Robotics (T-RO), 2025, 10.1109/TRO.2025.3577028. (2024年9月提交，2025年5月接收，2025年6月在线发表)

Abstract

This paper presents an effective and reliable pose tracking solution, termed ERPoT, for mobile robots operating in large-scale outdoor and challenging indoor environments, underpinned by an innovative prior polygon map. Especially, to overcome the challenge that arises as the map size grows with the expansion of the environment, the novel form of a prior map composed of multiple polygons is proposed. Benefiting from the use of polygons to concisely and accurately depict environmental occupancy, the prior polygon map achieves long-term reliable pose tracking while ensuring a compact form. More importantly, pose tracking is carried out under pure LiDAR mode, and the dense 3D point cloud is transformed into a sparse 2D scan through ground removal and obstacle selection. On this basis, a novel cost function for pose estimation through point-polygon matching is introduced, encompassing two distinct constraint forms: point-to-vertex and point-to-edge. In this study, our primary focus lies on two crucial aspects: lightweight and compact prior map construction, as well as effective and reliable robot pose tracking. Both aspects serve as the foundational pillars for future navigation across diverse mobile platforms equipped with different LiDAR sensors in varied environments. Comparative experiments based on the publicly available datasets and our self-recorded datasets are conducted, and evaluation results show the superior performance of ERPoT on reliability, prior map size, pose estimation error, and runtime over the other six approaches. The corresponding code can be accessed at https://github.com/ghm0819/ERPoT, and the supplementary video is at https://youtu.be/6XdcXyUrLKw.