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Friday, 25 May 2012 |
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MSc colloquium:
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Loop closing in SLAM using artificial intelligence
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Speaker:
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Berend Kupers
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Supervisor:
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prof. dr. R. Babuška, R. Prevel, MSc
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Location:
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Cor Biezeno room
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Time:
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14:00 until 14:30
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Abstract:
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One of the key questions that needs to be answered before service robots can be used in any natural environment is how to autonomously navigate through unknown environments. It is desired that the robot is able to build a map of the environment while at the same time localizing itself within this map. In past two decades many advances have been made to solve this Simultaneous Localization and Mapping (SLAM) problem. The positions of distinct points, called features, in the environment are observed with cameras or laser scanners and are stored in a map. Based on the movement of these features with respect to the robot, the estimate position of the robot can be improved. Many different algorithms to tackle the SLAM problem already exist, among which are adapted versions of the Rao-Blackwellized particle filter, the extended Kalman filter and the iterative closest point method. All these methods however run into problems when the robot returns to a known place after traversing a large cyclic trajectory. This problem, known as the loop closing problem, is caused by a large error in the estimate pose, which in turn causes the algorithm to assume the observed features are from an unknown part of the map instead of the already mapped area. A relatively new and promising approach to solve this loop closing problem is based on formulating the task of obtaining a single consistent map as an optimization problem and subsequently using techniques from artificial intelligence to optimize the estimate trajectory and the corresponding map. The most important approaches to solve the SLAM problem will be discussed as well the most promising solutions to the loop closing problem. The presentation will conclude with recommendations for future research.
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Other info:
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Literature colloquium
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