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Monocular visual-inertial odometry (VIO) is a low-cost solution to providehigh-accuracy, low-drifting pose estimation. However, it has been meetingchallenges in vehicular scenarios due to limited dynamics and lack of stablefeatures. In this paper, we propose Ground-VIO, which utilizes ground featuresand the specific camera-ground geometry to enhance monocular VIO performance inrealistic road environments. In the method, the camera-ground geometry ismodeled with vehicle-centered parameters and integrated into anoptimization-based VIO framework. These parameters could be calibrated onlineand simultaneously improve the odometry accuracy by providing stablescale-awareness. Besides, a specially designed visual front-end is developed tostably extract and track ground features via the inverse perspective mapping(IPM) technique. Both simulation tests and real-world experiments are conductedto verify the effectiveness of the proposed method. The results show that ourimplementation could dramatically improve monocular VIO accuracy in vehicularscenarios, achieving comparable or even better performance than state-of-artstereo VIO solutions. The system could also be used for the auto-calibration ofIPM which is widely used in vehicle perception. A toolkit for ground featureprocessing, together with the experimental datasets, would be made open-source(https://github.com/GREAT-WHU/gv_tools).

Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters