RELATIVE LASER SCANNER AND IMAGE POSE ESTIMATION FROM POINTS AND SEGMENTS

This paper presents an approach involving linear features for pose estimation. Here we are interesting in surveys mixing image and laser scanning, for metrological applications. Since data need to be registered with the best accuracy, we are faced to a 2D-3D pose estimation problem. In most cases, scenes contain numerous segments, which are good orientation clues. We use these segments to find pose. Therefore, targets are less prevalent for location and orientation estimation purpose. This means less field operations during data acquisition. Since some scenes with very few straight lines can leave insufficient spatial constraints, we reintroduce points. We can deal with feature points to reinforce the system. Then, the algorithm simultaneously minimizes an energy function managing distances between 3D points projection in images and image points, and distances on segments ends. Precise determination of primitives in 2D and 3D data leads to fine orientation. Using subpixelar regression after an edge detection gives high-quality estimates for 2D segments. In point clouds, 3D segments come from plane intersection. We discuss relative influence of features through uncertainty assessment