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

Abstract

To ensure unmanned autonomous movement of ground robotic means, it is required to accurately determine the position and orientationof the robot. The present study is related to the estimation of coordinates by comparing the scans of a laser scanning rangefinder in conditionsof semi-structed infrastructure and the absence of a global satellite communications signal. The existing methods of comparing scans havesignificant drawbacks in the conditions of movement over a semi-structured terrain, associated both with the processing time of data fromthe 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, wetake into account the mutual influence of all forces from each point of the scan and thus we obtain the resulting artificial potential field ofthe 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 takinginto account their direction. We estimate the orientation of the robot based on the sum of the vector products of the forces and distancesto the given forces acting on the points of the next scan. This method allows you to calculate the displacement of the robot between scansregardless of road conditions and terrain. This article presents the results of an experimental verification of the method on a mock-up of amobile robot equipped with a Velodyne HDL-32 LIDAR. We indicate the operating conditions of the method for a given LIDAR, as wellas the time spent on calculating the bias estimate. Given the peculiarities of the LIDAR, we present a method for eliminating the DopplerEffect (distortion) for the original point cloud. A comparative analysis of the developed method for integrating wheel odometry data, inertialand satellite navigation using the Extended Kalman Filter shows the applicability of this method to assess the position and orientation of therobot in conditions of its movement over rough terrain.