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2007

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

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Self-localization in non-stationary environments using omni-directional vision
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. c 2007 Elsevier B.V. All rights reserved.
Henrik Andreasson, André Treptow, Tom Ducke
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where RAS
Authors Henrik Andreasson, André Treptow, Tom Duckett
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