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

Andreasson, Henrik, Treptow, André and Duckett, Tom (2007) Self-localization in non-stationary environments using omni-directional vision. Robotics and Autonomous Systems, 55 (7). pp. 541-551. ISSN 0921-8890

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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.

Keywords:Localization, Omni-directional vision, Monte Carlo localization, particle filtering, local features
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
ID Code:28026
Deposited On:28 Jul 2017 08:57

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