Scan Matching for Navigation of a Mobile Robot in Semi-Structured Terrain Conditions

To ensure unmanned autonomous movement of ground robotic means, it is required to accurately determine the position and orientation of the robot. The present study is related to the estimation of coordinates by comparing the scans of a laser scanning rangefinder in conditions of semi-structed infrastructure and the absence of a global satellite communications signal. The existing methods of comparing scans have significant drawbacks in the conditions of movement over a semi-structured terrain, associated both with the processing time of data from the laser scanning rangefinder, and with the quality of the results obtained. The scan is preliminarily placed in a map consisting of cells. Each cell of around point scan is described by forces represented by the laws of physics or probability theory. In the cells of the map, we take into account the mutual influence of all forces from each point of the scan and thus we obtain the resulting artificial potential field of the scan. The position of the robot is estimated by the change in the number of acting forces of one scan per points of the next scan taking into account their direction. We estimate the orientation of the robot based on the sum of the vector products of the forces and distances to the given forces acting on the points of the next scan. This method allows you to calculate the displacement of the robot between scans regardless of road conditions and terrain. This article presents the results of an experimental verification of the method on a mock-up of a mobile robot equipped with a Velodyne HDL-32 LIDAR. We indicate the operating conditions of the method for a given LIDAR, as well as the time spent on calculating the bias estimate. Given the peculiarities of the LIDAR, we present a method for eliminating the Doppler Effect (distortion) for the original point cloud. A comparative analysis of the developed method for integrating wheel odometry data, inertial and satellite navigation using the Extended Kalman Filter shows the applicability of this method to assess the position and orientation of the robot in conditions of its movement over rough terrain.

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