Learning to Localize Using a LiDAR Intensity Map

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

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)605-616
Number of pages12
JournalProceedings of Machine Learning Research
StatePublished - 2018
Externally publishedYes
Event2nd Conference on Robot Learning, CoRL 2018 - Zurich, Switzerland
Duration: Oct 29 2018Oct 31 2018


  • Deep Learning
  • Localization
  • Map-based Localization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


Dive into the research topics of 'Learning to Localize Using a LiDAR Intensity Map'. Together they form a unique fingerprint.

Cite this