Learning to Localize Using a LiDAR Intensity Map


Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun ;
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:605-616, 2018.


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.

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