Vision-based loop closing for delayed state robot mapping

Abstract

This paper shows results on outdoor vision-based loop closing for simultaneous localization and mapping. Our experiments show that for loops of over 50 m, the pose estimates maintained with a delayed-state extended information filter are consistent enough to guarantee assertion of vision- based pose constraints for loop closure, provided no necessary information links are added to the estimator. The technique computes relative pose constraints via a robust least squares minimization of 3D point correspondences, which are in turn obtained from the matching of SIFT features over candidate image pairs. We propose a loop closure test that checks both for closeness of means and for highly informative updates at the same time.