Detection and Control of Mobile Robot Motion by Real-Time Computer Vision

Abstract

An approach is presented that combines dynamical models of 3D motion with geometric models of the scene and the laws of perspective projection to estimate all motion parameters necessary to control a mobile robot vehicle. The approach is demonstrated by autonomous con-trol of a jet propelled air-cushion vehicle, navigating through a technical environment with three degrees of motion freedom and performing a rendezvous maneuver with a passive partner. Features of the partner and other objects in the scene, the 3D shapes of which are known, are looked for and then tracked by the processors of a multimicroprocessor system. A sequential Kalman filter formulation is used to detect and to cope with variable feature visibility due to occlusion and motion while determining the complete relative motion state without inversion of the projection equations. A scheme is developed for always selecting those features for tracking which yield the best state estimate, the quality of which is demonstrated by physical docking with a static partner. The system operates at 0.13 seconds cycle time, half of which is spent for I/O operations. Experimental results are given.