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Multi-hypothesis localization with a rough map using multiple visual features for outdoor navigation

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We describe a method of mobile robot localization based on a rough map using stereo vision, which uses multiple visual features to detect and segment the buildings in the robot's field of view. The rough map is an inaccurate map with large uncertainties in the shapes, dimensions and locations of objects so that it can be built easily. The robot fuses odometry and vision information using extended Kalman filters to update the robot pose and the associated uncertainty based on the recognition of buildings in the map. We use a multi-hypothesis Kalman filter to generate and track Gaussian pose hypotheses. An experimental result shows the feasibility of our localization method in an outdoor environment.
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Document Type: Research Article

Affiliations: Department of Mechanical Engineering, Osaka University, Suita, Osaka 565-0871, Japan

Publication date: 2007-11-01

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