VINS-Motion: Tightly-coupled Fusion of VINS and Motion Constraint

Abstract

In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion constraints for improving the autonomous vehicles localization accuracy. Besides the prior information, IMU measurement residual, and visual measurement residual utilized in VINS, vehicle orientation/velocity constraint is first exploited to constitute motion residual. We minimize the sum of priors and Mahalanobis norms of three kinds of residuals to obtain a maximum posteriori estimation, thus increasing system consistency and accuracy. Stop detection is also added to help eliminate the abnormal jitter of the estimated poses during stopping, thus ensuring reasonability of the trajectory. The pro-posed approach is validated on public datasets and compared against state-of-the-art algorithms, which demonstrates that VINS-Motion achieves significantly higher positioning accuracy.