Hough transform based correction of mobile robot orientation
In most applications, a mobile robot must be able to determine its position and orientation in the environment using only own sensors. The problem of pose tracking can be seen as a constituent part of the more general navigation problem. Our proposed approach is able to track the mobile robot pose without environment model. It is based on combining histograms and Hough transform (HHT). While histograms for position tracking (x and y histograms) are extracted directly from local occupancy grid maps, angle histogram is obtained indirectly via Hough transformation combined with a non-iterative algorithm for determination of end points and length of straight-line parts contained in obtained histograms. Histograms obtained at the actual mobile robot pose are compared to histograms saved at previous mobile robot poses to compute position displacement and orientation correction. Orientation estimation accuracy greatly influences the position estimation accuracy and is crucial for a reliable mobile robot pose tracking. Sensors used for local occupancy grid generation are sonars but other exteroceptive sensors like a laser range finder can also be used. Test results with mobile robot Pioneer 2DX simulator show the capacity of this method.