ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using Lightweight Polygon Maps
Haiming Gao, Qibo Qiu, Hongyan Liu, Dingkun Liang, Chaoqun Wang, Xuebo Zhang
AI summary
Problem
As environments expand, traditional prior maps become too large for efficient storage and computation, while semantic-dependent methods lack generalization and reliability in diverse settings.
Approach
The method converts dense 3D LiDAR scans into sparse 2D obstacle points, constructs a lightweight multi-polygon prior map, and estimates pose using a novel point-polygon matching cost function with point-to-vertex and point-to-edge constraints.
Key results
- Lightweight multi-polygon map representation eliminates semantic dependencies and drastically reduces storage size.
- Novel point-polygon matching cost function with point-to-vertex and point-to-edge constraints improves data association.
- Outperforms six baseline methods in reliability, map size, pose estimation error, and runtime.
- Validated across diverse public and self-recorded datasets on varying platforms and LiDAR sensors.
Why it matters
Enables robust, real-time localization for resource-constrained mobile robots in large-scale, unstructured environments without relying on semantic cues.
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 perfor- mance of ERPoT on reliability, prior map size, pose estima- tion 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.