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

TL;DR

ERPoT introduces a lightweight, polygon-based prior map and a novel point-polygon matching method for reliable, long-term pose tracking of mobile robots in large-scale outdoor and indoor environments using LiDAR data.

Contribution

The paper proposes a new polygon-based prior map and a point-polygon matching cost function for improved pose tracking in mobile robots, emphasizing map compactness and reliability.

Findings

ERPoT outperforms six existing methods in reliability and accuracy.

The prior polygon map is more compact and scalable for large environments.

ERPoT achieves lower pose estimation error and faster runtime.

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.