Visual Place Recognition - What is it Good For?

Workshop at Robotics: Science and Systems (RSS)
June 19, 2016 --- Ann Arbor, Michigan, USA

Website: http://tinyurl.com/vprice-rss16
Submission via eMail to niko(dot)suenderhauf(at)roboticvision(dot)org

Submission deadline: May 8
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This workshop discusses novel concepts and ideas for robust vision‐based place recognition in severely changing environments. Such changes – induced e.g. by the time of day, weather or seasonal effects as well as human activity – are a ubiquitous challenge for all autonomous systems aiming at long‐term operations in both indoor and outdoor settings, and are highly relevant to visual neuroscience.

This year’s edition of the workshop puts a focus on contributions that show, discuss, and evaluate visual place recognition working in real robotic systems, in concert with mapping or SLAM, or embedded into a real-world application.

The workshop features a tutorial that introduces the basic principles and the state of the art to participants without previous experience in the field. Invited talks and a number of contributed talks discuss the newest developments, concepts and ideas in the areas covered by the workshop topics. We will continue to run a place recognition challenge in association with the workshop, building on its inaugural success in 2015.

Important Dates:
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Submission Deadline: May 8, 2016 (anywhere on the planet)
Acceptance Notification: May 15, 2016
Workshop Date: June 18, 2016


Paper Submissions:
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Papers will be presented in a short talk (expect 3-5 minutes) followed by a poster session on individual posters.

This year’s edition of the workshop puts a focus on contributions that show, discuss, and evaluate visual place recognition working in real robotic systems, in concert with mapping or SLAM, or embedded into a real-world application.
Topics of interest to this workshop include, but are not necessarily limited to:
Novel techniques for change-invariant whole image matching
Approaches for invariant image feature learning
Learning, modelling, and predicting systematic or repeating appearance changes over time
Learning stable / non-changing environmental features
Exploiting semantic information for long-term place recognition
Novel concepts of incorporating uncertain place recognition in SLAM / navigation systems
Standardized benchmarks and long-term datasets in changing environments

We explicitly encourage the submission of papers describing work in progress, preliminary results or novel concepts.

Organizers:
Dr Niko Sünderhauf, Assoc Prof Michael Milford, Assoc Prof Ben Upcroft, Prof Peter Corke
Australian Centre for Robotic Vision (ACRV), Queensland University of Technology (QUT), Australia

Dr Peer Neubert
Chemnitz University of Technology, Germany