Vehicle Positioning in GNSS-Deprived Urban Areas by Stereo Visual-Inertial Odometry

Abstract

Accurate and continuous positioning in global navigation satellite system (GNSS) deprived urban areas is crucial for autonomous vehicles and mobile mapping systems. To achieve this goal, we propose a stereo visual-inertial odometry approach using a multistate constraint Kalman filter (MSCKF). In contrast with the conventional MSCKF, in which an inertial navigation system (INS) propagates the vehicle motion, and then the propagation is corrected by measurements of salient features extracted from images of a single camera, we update the propagation with observations extracted from images of a stereo pair of cameras. This way, additional constraints across the stereo pairs of images are exploited to improve the pose estimation. Experimental results on several KITTI datasets show that the stereo MSCKF outperforms the mono one achieving an average positioning error of 0.9% of the trajectory length compared to 1.3% for the mono approach and 2.5% for INS-only integration. These results show that visual-inertial odometry has a promising potential for vehicle positioning in short periods of GNSS signal outage.