Authors V.N. Kazmin
Month, Year 01-02, 2017 @en
Index UDC 007:621.865.8
Abstract Approach for solving central in autonomous robotics SLAM problem for moving robotic ob-jects using data of onboard sensors, based on Monte Carlo localization, proposed and considered. In this method the particle filter is used to recursively construct posterior conditional probability density function from previous robot’s state and sensors measurement. A feature of the implemen-tation of the method is the use of the detected in rangefinder data planes for measuring the robot position relative to the map, by comparing them with those already in the database and solve a system of equations constructed on the parameters of found pairs corresponding to each other plane. When navigating, in addition to the space geometry, brightness coloring of segmented planes is used in order to improve the accuracy and resolution of geometrically uncertain situations. After determining the position of the robot, founded in the frame planes are entered into the database and averaged with the existing information, and they are used for navigation in the subsequent cycles of the algorithm. The results of the work of created algorithms and software, solving the task in the rate of motion of the robot in the real world, are shown; errors and performance are estimated. Based on the analysis of theoretical and experimental studies it concluded that the proposed approach provides a transition from large amounts of original range-finder data to a compact description of an environment and can effectively solve the problem of autonomous motion control of mobile robots and unmanned aerial vehicles.

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Keywords Mobile robot; unmanned aerial vehicle; vision system; particle filter; localization; environ-ments model reconstruction; Monte Carlo localization.
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