In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. Localization is then conducted through an efficient convolutional matching between the embeddings. Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments. Our experiments illustrate the performance of the proposed approach over a large-scale dataset consisting of over 4000km of driving.
@article{arxiv.2012.10902,
title = {Learning to Localize Using a LiDAR Intensity Map},
author = {Ioan Andrei Bârsan and Shenlong Wang and Andrei Pokrovsky and Raquel Urtasun},
journal= {arXiv preprint arXiv:2012.10902},
year = {2020}
}
Comments
12 pages, 7 figures, 5 tables; Presented at the 2nd Conference on Robot Learning (CoRL), 2018