Self-localization in non-stationary environments using omni-directional vision

Authors:
Henrik Andreasson;André Treptow;Tom Duckett
Affiliations:
Centre for Applied Autonomous Sensor Systems, Department of Technology, Örebro University, SE-70182 Örebro, Sweden;University of Tübingen, Department of Computer Science WSI-RA, Tübingen, Germany;Centre for Applied Autonomous Sensor Systems, Department of Technology, Örebro University, SE-70182 Örebro, Sweden
Venue:
Robotics and Autonomous Systems
Year:
2007

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Abstract

This paper presents an image-based approach for localization in non-static environments using local feature descriptors, and its experimental evaluation in a large, dynamic, populated environment where the time interval between the collected data sets is up to two months. By using local features together with panoramic images, robustness and invariance to large changes in the environment can be handled. Results from global place recognition with no evidence accumulation and a Monte Carlo localization method are shown. To test the approach even further, experiments were conducted with up to 90% virtual occlusion in addition to the dynamic changes in the environment.