A robust pose graph approach for city scale LiDAR mapping
Sheng Yang, Xiaoling Zhu, Xing Nian, Lu Feng, Xiaozhi Qu, Teng Ma
Abstract
This paper presents a method for reconstructing globally consistent 3D High-Definition (HD) maps at city scale. Current approaches for eliminating cumulative drift are mainly based on the pose graph optimization under the constraint of scan-matching factors. The misaligned edges in the graph may have negative impacts on the results. To address this problem and further handle inconsistency caused by multi-task acquisitions in urban environments, we introduce a refined structure of the factor graph considering systematical initialization bias, where the scan-matching factors are twice validated through a novel classifier and a robust optimization strategy. In addition, we incorporate a multi-hypothesis extended Kalman filter (MH-EKF) to remove dynamic objects. Quantitative experimental results demonstrate that the proposed method outperforms state-of-the-art techniques in terms of map quality.
BibTeX
@inproceedings{iros2018_arobustposegraph,
title = {A robust pose graph approach for city scale LiDAR mapping},
author = {Sheng Yang and Xiaoling Zhu and Xing Nian and Lu Feng and Xiaozhi Qu and Teng Ma},
booktitle = {IROS 2018},
year = {2018}
}