3D reconstruction is an important research area in computer vision. With the wide spectrum of camera geometry constraints, a general solution is still open. In this dissertation, the topic of 3D reconstruction is addressed under several special constraints on camera geometry, and the 3D reconstruction techniques developed under these constraints have been applied to a robotic navigation scenario. The robotic navigation problems addressed include automatic camera calibration, visual servoing for navigation control, obstacle detection, and 3D model acquisition and extension.
The problem of visual servoing control is investigated under the assumption of a structured environment where parallel path boundaries exist. A visual servoing control algorithm has been developed based on geometric variables extracted from this structured environment. This algorithm has been used for both automatic camera calibration and navigation servoing control. Close to real time performance is achieved.
The problem of qualitative and quantitative obstacle detection is addressed with a proposal of three algorithms. The first two are purely qualitative in the sense that they only return yes/no answers. The third is quantitative in that it recovers height information for all the points in the scene. Three different constraints on camera geometry are employed. The first algorithm assumes known relative pose between cameras; the second algorithm is based on completely unknown camera relative pose; the third algorithm assumes partial calibration. Experimental results are presented for real and simulated data, and the performance of the three algorithms under different noise levels are compared in simulation.
Finally the problem of model acquisition and extension is studied by proposing a 3D reconstruction algorithm using homography mapping. It is shown that given four coplanar correspondences, 3D structures can be recovered up to two solutions and with only one uniform scale factor, which is the distance from the camera center to the 3D plane formed by the four 3D points corresponding to the given four correspondences in the two camera planes. It is also shown that this algorithm is optimal in terms of the number of minimum required correspondences and in terms of the assumption of internal calibration.
