Pattern Recognition Letters
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In this thesis, we handle the problem of extracting 3D information from multiple images of a robotic work site in the context of teleoperation. A human operator determines the virtual path of a robotic vehicle and our mission is to provide him with the sequence of images that should be seen by the teleoperated robot moving along this path. The environment, in which the robotic vehicle moves, has a planar ground surface. In addition, a set of wide baseline images are available for the work site. This implies that a small number of points may be visible in more than two views. Moreover, camera parameters are known approximately. According to the sensor error margins, the parameters read lie within some range. Obstacles of different shapes are present in such an environment. In order to generate the sequence, this ground plane as well as the obstacles must be represented. The perspective image of the ground plane can be obtained through a homography matrix. This is done through the virtual camera parameters and the overhead view of the work site. In order to represent obstacles, we suggest different methods; these are volumetric and planar. Our algorithm to represent obstacles starts with detecting junctions. This is done through a new fast junction detection operator we propose. This operator provides the location of the junction as well as the orientations of the edges surrounding it. Junctions belonging to the obstacles are identified against those belonging to the ground plane through calculating the inter-image homography matrices. Fundamental matrices relating images can be estimated roughly through the available camera parameters. Strips surrounding epipolar lines are used as a search range for detecting possible matches. We introduce a novel homographic correlation method to be applied among candidates by reconstructing the planes of junctions in space. Two versions of homographic correlation are proposed; these are SAD and VNC. Both versions achieve matching results that outperform non-homographic correlation. The match set is then turned into a set of 3D points through triangulation. At this point, we propose a hierarchical structure to cluster points in space. This results in bounding boxes containing obstacles. A more accurate volumetric representation for the obstacle can be achieved through a voxelization approach. Another representation is suggested. That is to represent obstacles as planar patches. This is done through mapping among original and synthesized images. Finally, steps of the different algorithms presented throughout the thesis are supported by examples to show the usefulness we claim of our approaches.