Relative Pose Estimation for Instrumented, Calibrated Imaging Platforms

摘要

Recent efforts in robust estimation of the two-view relation have focused on uncalibrated cameras with no prior knowledge of pose. However, in practice robotic vehicles that perform image-based navigation and mapping typically 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 calibrated 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 moments to describe affine invariant features. Third, we robustly estimate the essential 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.