Relative Pose Estimation for Instrumented, Calibrated Imaging Platforms

Recent efforts in robust estimation of the two-view relation have fo- cused on uncalibrated cameras with no prior knowledge of pose. However, in practice robotic vehicles that perform image-based navigation and mapping typi- cally do carry a calibrated camera and pose sensors; this additional knowledge is currently not being exploited. This paper presents three contributions in using vision with instrumented and cal- ibrated platforms. First, we improve the performace of the correspondence stage by using uncertain measurements from egomotion sensors to constrain possible matches. Second, we assume wide-baseline conditions and propose Zernike mo- ments to describe affine invariant features. Third, we robustly estimate the essen- tial matrix with a new 6-point algorithm. Our solution is simpler than the minimal 5-point one and, unlike the linear 6-point solution, does not fail on planar scenes. While the contributions are general, we present structure and motion results from an underwater robotic survey.