Abstract :
When a camera is used to provide the navigational parameters in autonomous vehicle operations, it is subjected to unexpected movements or vibrations of the mounting platform. This paper presents a framework for analyzing the effect of uncontrollable camera movements on the navigational parameters, in particular on the range and-heading angle in vision-based vehicle tracking. The noise introduced by the platform movements is modeled in two ways: camera noise approach and image noise approach. The parameter space of the camera is divided into a controllable subspace consisting of its height and depression angle, and an uncontrollable subspace consisting of the tracked object coordinates and rotation angle errors. A consistent detectable region is then obtained such that the tracked object is always seen by the camera. Based on this region, a reliable region consisting of no singularity points is established so that the range error does not become infinity. The optimum parameters of the controllable subspace with respect to the uncontrollable subspace are found by employing two estimation schemes: (a) the mini-max estimator to provide the worst case effect, and (b) the minimum-mean-square estimator to provide the average or overall effect. From the results obtained, it is shown how an optimum imaging geometry of a monocular vision-based tracking system can be designed in order to satisfy prescribed levels of range and heading angle errors
Keywords :
computer vision; computerised navigation; errors; estimation theory; mobile robots; noise; tracking; autonomous vehicle; camera movement errors; camera noise approach; consistent detectable region; controllable subspace; image noise approach; mini-max estimator; minimum-mean-square estimator; monocular vision-based tracking system; mounting platform; navigational parameters; optimum imaging geometry; range error; rotation angle errors; tracked object coordinates; uncontrollable camera movements; uncontrollable subspace; unexpected movements; vibrations; vision-based vehicle tracking; worst case effect; Cameras; Error correction; Machine vision; Mobile robots; Navigation; Object detection; Quantization; Remotely operated vehicles; Tracking; Working environment noise;