Learning to Localize Using a LiDAR Intensity Map

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

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arXiv · 2012.10902

Learning to Localize Using a LiDAR Intensity Map

Ioan Andrei Bârsan·Shenlong Wang·Andrei Pokrovsky·Raquel Urtasun

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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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BibTeX RIS

Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun. 2020-12-20. Learning to Localize Using a LiDAR Intensity Map. https://arxiv.org/abs/2012.10902

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