Discrete pose space estimation to improve ICP-based tracking

Iterative closest point (ICP)-based tracking works well when the interframe motion is within the ICP minimum well space. For large interframe motions resulting from a limited sensor acquisition rate relative to the speed of the object motion, it suffers from slow convergence and a tendency to be stalled by local minima. A novel method is proposed to improve the performance of ICP-based tracking. The method is based upon the bounded Hough transform (BHT) which estimates the object pose in a coarse discrete pose space. Given an initial pose estimate, and assuming that the interframe motion is bounded in all 6 pose dimensions, the BHT estimates the current frame´s pose. On its own, the BHT is able to track an object´s pose in sparse range data both efficiently and reliably, albeit with a limited precision. Experiments on both simulated and real data show the BHT to be more efficient than a number of variants of the ICP for a similar degree of reliability. A hybrid method has also been implemented wherein at each frame the BHT is followed by a few ICP iterations. This hybrid method is more efficient than the ICP, and is more reliable than either the BHT or ICP separately.