A combined particle filter and deterministic approach for underwater object localization using markers

In order to perform autonomous manipulation in underwater missions, a robust object localization technique is crucial. This paper proposes a monocular vision based pose estimation algorithm that is intended to be a part of a supervisory manipulation system for underwater intervention missions such as DIFIS. The proposed technique incorporates a deterministic scheme into a particle filter framework in solving simultaneously the marker correspondence and pose estimation problem. Preliminary simulation results have shown that the deterministic particle filter outperforms the standard particle filter in pose estimation even in the presence of minimal noise and occlusion. However, the accuracy and speed offered by the algorithm are still insufficient for close range intervention tasks. Improvements and further testing are required before the algorithm could be used on an I-AUV.