Extreme 3D Image Navigation in Mobile Robotics

Abstract

The actual problem of determining all six coordinates of the current position of a mobile robot (unmanned aerial vehicle) from 3D-range-finding images (point clouds) generated by an onboard 3D laser sensor when moving (flying) in an unknown environment is considered. An extreme navigation algorithm based on using multidimensional optimization methods is proposed. The rules for calculating the difference between 3D images of the external environment used for optimization of the functional are described. The form of the functional of the difference of 3D images for different environments (premises, industrial-urban environment, rugged and wooded areas) has been investigated. Requirements for the characteristics of the sensor and the geometry of the external environment are formulated, the fulfillment of which ensures the correct formulation and solution of the problem of extreme navigation. The optimal methods of scanning the surrounding space are described and the conditions are substantiated, the fulfillment of which ensures the solution of the navigation problem by the proposed algorithm in real time (at the rate of movement) when processing 3D images formed by modern 3D laser sensors. In particular, the dependence between the frequency of formation of 3D images and the angular and linear velocities of motion is described, which ensures that the functional of the difference of 3D images falls into the multidimensional interval of unimodality, which guarantees a direct search of global minimum in real time. Various methods of direct search for the global minimum of the functional are tested and the fastest for the case under consideration are selected. The accuracy of solving the navigation problem is estimated and a method is proposed to reduce the accumulated error, based on using an older 3D image for correcting the calculated value of the current coordinates, which has an intersection of the view area with the current view area. The proposed method, which is a modification of the reference image method, allows reduce the total error, which grows in proportion to the number of cycles of solving the extreme navigation problem, to values that ensure the autonomous functioning of transport robots and UAVs in previously unprepared and unknown environments. The effectiveness of the proposed algorithmic and developed software and hardware for extreme navigation is confirmed by field experiments carried out in real conditions of various environments.