Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due to limited dynamics and lack of stable features. 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 simulation tests and real-world experiments 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 or even better performance than state-of-art stereo VIO solutions. The system could 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, would be made open-source (https://github.com/GREAT-WHU/gv_tools).

翻译：单目视觉惯性里程计（VIO）是一种低成本的高精度低漂移位姿估计方案。然而，由于车辆场景中运动动力学受限且缺乏稳定特征点，该方法面临挑战。本文提出Ground-VIO算法，通过利用地面特征及特定的相机-地面几何关系，提升单目VIO在真实道路环境中的性能。该方法采用以车辆为中心的参数对相机-地面几何进行建模，并将其集成到基于优化的VIO框架中。这些参数可通过在线标定实时优化，同时通过提供稳定的尺度感知能力提升里程计精度。此外，我们设计了专用的视觉前端，利用逆透视映射（IPM）技术稳定提取并跟踪地面特征。通过仿真测试与真实实验验证了该方法的有效性。结果表明，该方法能显著提升车辆场景下单目VIO的精度，达到甚至超越当前最先进的立体VIO方案。该系统还可用于车辆感知中广泛使用的IPM自动标定。地面特征处理工具包及相关实验数据集将进行开源（https://github.com/GREAT-WHU/gv_tools）。