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A novel algorithm for pose and motion estimation using corresponding features and a Digital Terrain Map is proposed. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables the elimination of the ambiguity present in vision-based algorithms for motion recovery. As a consequence, the absolute position and orientation of a camera can be recovered with respect to the external reference frame. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. Explicit reconstruction of the 3D world is not required. When considering a number of feature points, the resulting constraints can be solved using nonlinear optimization in terms of position, orientation, and motion. Such a procedure requires an initial guess of these parameters, which can be obtained from dead-reckoning or any other source. The feasibility of the algorithm is established through extensive experimentation. Performance is compared with a state-of-the-art alternative algorithm, which intermediately reconstructs the 3D structure and then registers it to the DTM. A clear advantage for the novel algorithm is demonstrated in variety of scenarios.
Pose estimation, vision-based navigation, DTM, structure from motion.
H?ctor P. Rotstein, Ehud Rivlin, Ronen Lerner, "Pose and Motion Recovery from Feature Correspondences and a Digital Terrain Map", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1404-1417, September 2006, doi:10.1109/TPAMI.2006.192
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