In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360{\deg} LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance.
@article{arxiv.2003.02392,
title = {PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization},
author = {Wei Wang and Bing Wang and Peijun Zhao and Changhao Chen and Ronald Clark and Bo Yang and Andrew Markham and Niki Trigoni},
journal= {arXiv preprint arXiv:2003.02392},
year = {2021}
}