Skip to footer
Home Research Artificial Intelligence / Machine Learning Learning to Localize Using a LiDAR Intensity Map

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.

Authors

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

Conference

CORL 2018

Full Paper

‘Learning to Localize Using a LiDAR Intensity Map’ (PDF)

Uber ATG

Comments
Previous article Deep Multi-Sensor Lane Detection
Next article Joint Mapping and Calibration via Differentiable Sensor Fusion
Avatar
I'm a graduate student at the University of Toronto doing research on combining classic geometric methods with learning for computer vision. My supervisor is Prof. Raquel Urtasun. At the same time, I am working full-time at Uber ATG Toronto (also with Raquel) to apply my research to the real-world challenges of self-driving cars. Before coming to UofT, I did my Master's at ETH Zurich, writing my Master's Thesis on large-scale dense mapping under Prof. Andreas Geiger's supervision. I am originally from Brașov, Romania.
Shenlong Wang
Shenlong Wang is research scientist at Uber ATG Toronto working on the development of self-driving cars. He is also a PhD student at University of Toronto. His advisor is Prof. Raquel Urtasun. He has a broad interest in computer vision, machine learning and robotics. He is particularly interested in 3D vision and deep structured models.
Avatar
Andrei Pokrovsky is a researcher/engineer at Uber Advanced Technologies Group Toronto.
Raquel Urtasun
Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine