Particle Filter Based Robust Recognition and Pose Estimation of 3D Objects in a Sequence of Images

A particle filter framework of multiple evidence fusion and model matching in a sequence of images is presented for robust recognition and pose estimation of 3D objects. It attempts to challenge a long-standing problem in robot vision, so called, how to ensure the dependability of its performance under the large variation in visual properties of a scene due to changes in illumination, texture, occlusion, as well as camera pose. To ensure the dependability, we propose a behavioral process in vision, where possible interpretations are carried probabilistically in space and time for further investigations till they are converged to a credible decision by additional evidences. The proposed approach features 1) the automatic selection and collection of an optimal set of evidences based on in-situ monitoring of environmental variations, 2) the derivation of multiple interpretations, as particles representing possible object poses in 3D space, and the assignment of their probabilities based on matching the object model with evidences, and 3) the particle filtering of interpretations in time with the additional evidences obtained from a sequence of images. The proposed approach has been validated by the stereo-camera based experimentation of 3D object recognition and pose estimation, where a combination of photometric and geometric features are used for evidences.