Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
413325 | Robotics and Autonomous Systems | 2007 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Henrik Andreasson, André Treptow, Tom Duckett,