Error Analysis in Stereo Determination of 3-D Point Positions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
VITS-A Vision System for Autonomous Land Vehicle Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Vision and navigation for the Carnegie-Mellon navlab
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Autonomous Robotic Vehicle Road Following
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing and computer vision: an introduction to theory and implementations
Digital image processing and computer vision: an introduction to theory and implementations
Efficient algorithms for obstacle detection using range data
Computer Vision, Graphics, and Image Processing
Optimal imaging geometry for vision-based tracking systems
Optimal imaging geometry for vision-based tracking systems
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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.