Simultaneous stereo-motion fusion and 3-D motion tracking

Presents a new framework for combining maximum likelihood (ML) stereo-motion fusion with adaptive iterated extended Kalman filtering (IEKF) for 3-D motion tracking. The ML stereo-fusion step, with two stereo-pairs, generates observations of 3-D feature matches to be used by the IEKF step. The IEKF step, in turn, computes updated 3-D motion parameter estimates to be used by the ML stereo-motion fusion step. The covariance of the observation noise process is regulated by the value of the ML cost function to address occlusion related problems. The proposed simultaneous approach is compared with performing the 3-D feature correspondence estimation and the Kalman filtering separately using simulated stereo imagery