Learning to Localize Using a LiDAR Intensity mAP
Abstract
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.
Cite
Text
Barsan et al. "Learning to Localize Using a LiDAR Intensity mAP." Conference on Robot Learning, 2018.Markdown
[Barsan et al. "Learning to Localize Using a LiDAR Intensity mAP." Conference on Robot Learning, 2018.](https://mlanthology.org/corl/2018/barsan2018corl-learning/)BibTeX
@inproceedings{barsan2018corl-learning,
title = {{Learning to Localize Using a LiDAR Intensity mAP}},
author = {Barsan, Ioan Andrei and Wang, Shenlong and Pokrovsky, Andrei and Urtasun, Raquel},
booktitle = {Conference on Robot Learning},
year = {2018},
pages = {605-616},
url = {https://mlanthology.org/corl/2018/barsan2018corl-learning/}
}