ECCV 2022 Workshop on

Map-Based Localization for Autonomous Driving

Workshop Information

  • When: Sunday, October 23, 2022.

  • Where: Hybrid, online, and at ECCV in Tel Aviv, Israel (InterContinental David, Meeting Room 3)

  • Schedule:


    • 09:00 - 09:10: Introduction to the Workshop [slides] [video]

    • 09:10 - 09:55: Invited Keynote Talk: Yuning Chai [no slides available] [video]

    • 09:55 - 10:40: Invited Keynote Talk: Abhinav Valada [no slides available] [video]

    • 10:40 - 11:05: Coffee break

    • 11:05 - 11:50: Invited Keynote Talk: Andrew Davison [slides] [video]

    • 11:50 - 12:10: Presentation-only paper "PICCOLO: Point Cloud-Centric Omnidirectional Localization" (ICCV 2021) [slides] [video]

    • 12:10 - 12:30: Presentation-only paper "LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning" (ICASSP 2022) [slides] [video]

    • 12:30 - 13:30: Lunch break


    • 13:30 - 13:40: Re-Localization Challenge Introduction [slides] [video]

    • 13:40 - 14:00: 1st Place Re-Localization Challenge [slides] [video]

    • 14:00 - 14:20: 2nd Place Re-Localization Challenge [slides] [video]

    • 14:20 - 14:40: Presentation-only paper "Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency" (WACV 2022) [slides] [video]

    • 14:40 - 15:25: Invited Keynote Talk: Henning Lategahn [no slides available] [video]

    • 15:25 - 15:40: Coffee break

    • 15:40 - 16:25: Invited Keynote Talk: Philipp Krähenbühl [no slides available] [video]

    • 16:25 - 16:35: Closing Remarks


In this workshop, we will discuss the importance of map-based real-time localization for autonomous driving. We will focus on the problem of map generation and how to keep the maps up-to-date as well as which sensor technologies can be used. Furthermore, the workshop will host the 3rd re-localization challenge for autonomous driving based on the 4Seasons dataset.


We invite researchers to submit original research papers in the area of mapping and map-based localization for autonomous driving, which will be peer-reviewed and published in the workshop proceedings if accepted. Additionally, to this publication track, we offer a presentation-only track for papers that have been previously published in peer-reviewed venues.

Paper submission (both tracks): August 7, 2022

Acceptance notification (both tracks): August 16, 2022

Camera-ready (publication track): August 19, 2022

Challenge submission: October 14, 2022


The workshop topics include (but are not limited to):

  • Simultaneous Localization and Mapping

  • Localization and re-localization under challenging conditions where current methods fail (weather changes, day versus night, etc.)

  • Self-supervised/semi-supervised learning

  • Domain adaptation

  • Mapping

Detailed Description

This is the 3rd workshop on map-based localization in the context of autonomous driving (AD).

By map-based localization, we understand the problem of accurately localizing (estimating the ego-position and -orientation) an autonomous vehicle in real-time in a pre-built map. Centimeter-accurate continuous global localization is a key feature for AD as it allows to position and tracks the ego-vehicle precisely within an HD map which contains important information about the environment. Being able to accurately localize within a pre-build map using standard perceptive sensors (e.g. camera, radar, LiDAR) extends the operation to GNSS-denied environments such as urban canyons or tunnels.

This task comprises several challenges including the question on how to create maps that are compressed in size and guarantee reliable localization independent of environmental conditions (e.g. weather, lighting, the season of the year) as well as keeping them up-to-date. Another aspect is the right sensor choice (with respect to robustness, accuracy, price) for both map generation and online localization.

Besides discussing the importance of map-based localization with experts from academia and industry, the workshop will host the 3rd re-localization challenge for autonomous driving based on the 4Seasons dataset.