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

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it encounters challenges in vehicular scenarios, as the restricted motion of a ground vehicle could lead to degraded observability, and a lack of stable features might occur in dynamic road environments. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both real-world experiments and tests on public datasets are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable performance to state-of-art stereo VIO solutions and showing good robustness in challenging conditions. The system can also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, has been made open-source.