Pole-like Objects Mapping and Long-Term Robot Localization in Dynamic Urban Scenarios

TL;DR

This paper presents a LiDAR-based long-term localization method for urban robots using pole-like objects as stable landmarks, leveraging semantic segmentation and map matching to improve accuracy without high-precision maps.

Contribution

The novel approach combines semantic segmentation with pole/trunk object extraction for long-term localization in dynamic urban environments, avoiding the need for detailed point cloud maps.

Findings

Outperforms current state-of-the-art in localization accuracy

Achieves stable pose estimation at 2Hz in long-term scenarios

Demonstrates effectiveness on campus dataset

Abstract

Localization on 3D data is a challenging task for unmanned vehicles, especially in long-term dynamic urban scenarios. Due to the generality and long-term stability, the pole-like objects are very suitable as landmarks for unmanned vehicle localization in time-varing scenarios. In this paper, a long-term LiDAR-only localization algorithm based on semantic cluster map is proposed. At first, the Convolutional Neural Network(CNN) is used to infer the semantics of LiDAR point clouds. Combined with the point cloud segmentation, the long-term static objects pole/trunk in the scene are extracted and registered into a semantic cluster map. When the unmanned vehicle re-enters the environment again, the relocalization is completed by matching the clusters of the local map with the clusters of the global map. Furthermore, the continuous matching between the local and global maps stably outputs the global pose at 2Hz to correct the drift of the 3D LiDAR odometry. The proposed approach realizes localization in the long-term scenarios without maintaining the high-precision point cloud map. The experimental results on our campus dataset demonstrate that the proposed approach performs better in localization accuracy compared with the current state-of-the-art methods. The source of this paper is available at: http://www.github.com/HITSZ-NRSL/long-term-localization.