Learning to Localize Using a LiDAR Intensity Map

• URL: http://arxiv.org/abs/2012.10902v1
• Date: Sun, 20 Dec 2020 11:56:23 GMT
• Title: Learning to Localize Using a LiDAR Intensity Map
• Authors: Ioan Andrei B\^arsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun
• Abstract summary: 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. Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments.
• Score: 87.04427452634445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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