Improved frame-to-frame pose tracking during vision-only SLAM/SFM with a tumbling target

A hybrid algorithm for real-time frame-to-frame pose estimation during monocular vision-only SLAM/SFM is presented. The algorithm combines concepts from two existing approaches to pose tracking, Bayesian estimation methods and measurement inversion techniques, to achieve in real-time a feasible, smooth estimate of the relative pose between a robotic platform and a tumbling target. It is assumed that no a priori information about the target is available, and that only a monocular camera is available for measuring the relative motion of the target with respect to the robotic platform. The rationale for a hybrid approach is explained, and an algorithm is presented. A specific implementation using a modified Rao-Blackwellised particle filter is described and tested. Results from both numerical simulations and field experiments are included which demonstrate the performance and viability of the hybrid approach. The hybrid approach to pose estimation described here is applicable regardless of the method by which the map/reconstruction is estimated.