This paper addresses the design of a model-based 3D object pose estimation algorithm, which is one of the major techniques to develop a robust visual tracking system. The proposed algorithm first extracts line features of a captured image by using edge detection and Hough transform techniques. Given a CAD model of the object-of-interest, the 3D pose of the object can then be estimated through a nonlinear model fitting process.
Relation:
Proceedings of The 2015 International Automatic Control Conference (CACS2015)
