ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using Lightweight Polygon Maps

2024-09-23

AI Summary

This work addresses the challenges of LiDAR-based pose tracking for mobile robots in large-scale indoor–outdoor environments—namely, map inflation, high computational overhead, and insufficient long-term robustness. We propose a pure-LiDAR localization method leveraging a lightweight polygonal prior map. By performing ground removal and obstacle filtering, dense 3D point clouds are compressed into sparse 2D scans, enabling construction of a compact yet highly representative polygonal map. We introduce a novel point-to-polygon matching cost function incorporating dual constraints—point-to-vertex and point-to-edge—significantly improving geometric matching accuracy and tracking stability in large-scale scenarios. Evaluated on public and custom datasets, our method reduces pose estimation error by 21%–38% compared to six state-of-the-art approaches, shrinks prior map size by over 90%, achieves 2.3× higher real-time performance, and lowers long-term tracking failure rate by 76%.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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/cseml5FrW1Q.

Problem

• Develops lightweight polygon maps for mobile robot pose tracking
• Enables reliable pose tracking in large outdoor and indoor environments
• Introduces point-polygon matching for efficient LiDAR-based pose estimation

Innovation

• Lightweight polygon maps for compact representation
• Pure LiDAR mode with sparse 2D scans
• Point-polygon matching cost function for pose estimation