ICCV 2021 Workshop on
Map-Based Localization for Autonomous Driving
When: October 11, 2021
Where: Virtual (ICCV 2021): https://youtu.be/p00-KOInGmc
Time: 3:00 - 8:30 PM UTC+2
3:00 - 3:10 PM: Introduction to the Workshop
3:10 - 3:50 PM: Invited Talk: Michael Milford
3:50 - 4:30 PM: Invited Talk: Simon Lynen
4:30 - 5:10 PM: Invited Talk: Alex Kendall
5:10 - 5:40 PM: Break
5:40 - 5:50 PM: Introduction to the Challenge
5:50 - 6:30 PM: Challenge Talks
6:30 - 7:10 PM: Invited Talk: Felix Heide
7:10 - 7:50 PM: Invited Talk: Torsten Sattler
7:50 - 8:30 PM: Invited Talk: Wolfram Burgard
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 2nd re-localization challenge for autonomous driving based on the 4Seasons dataset.
Paper submission due: July 30, 2021
Acceptance notification: August 13, 2021
Camera ready: August 16, 2021
Challenge deadline: October 01, 2021
The workshop topics include (but are not limited to):
This is the 2nd 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 2nd re-localization challenge for autonomous driving based on the 4Seasons dataset recorded by Artisense in collaboration with the Technical University of Munich using the Artisense VINS.