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