Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.
@article{arxiv.1712.02165,
title = {LocNet: Global localization in 3D point clouds for mobile vehicles},
author = {Huan Yin and Li Tang and Xiaqing Ding and Yue Wang and Rong Xiong},
journal= {arXiv preprint arXiv:1712.02165},
year = {2022}
}