Learning to Localize Using a LiDAR Intensity Map

Ioan Andrei Barsan; Shenlong Wang; Andrei Pokrovsky; Raquel Urtasun

CoRL 20180 citations

Learning to Localize Using a LiDAR Intensity Map

Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun

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.

BibTeX

@inproceedings{corl2018_learningtolocali,
  title = {Learning to Localize Using a LiDAR Intensity Map},
  author = {Ioan Andrei Barsan and Shenlong Wang and Andrei Pokrovsky and Raquel Urtasun},
  booktitle = {CoRL 2018},
  year = {2018}
}