In the article we propose estimation algorithm for mobile robot movement in surrounding space. Surrounding space geometry is characterized by proximity sensors data samplings from mobile robot. Both the sampling built at previous time moments and the map can serve as a standart. The algorithm is invariant to isotropic dimensional scaling of the samplings (the map), which makes it possible to use samplings with different units of measure ans maps of various scales. The algorithm is based on the idea of Hough transform: translation from the measurement space to line parameters space is carried out. In this space estimation tasks for rotation, scaling and translation are solved separately, decomposing the task of mobile robot position estimation into three of smaller order independent tasks. Proposed algorithm is notable for robustness against measurement noise.
Mobile robot, Hough transform, robot movement estimation, mobile robot orientation, simultaneous localization and mapping, SLAM.
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