Ground-VIO：具有相机-地面几何参数在线标定的单目视觉惯性里程计
Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters
AI总结：
针对车辆场景中单目VIO精度低的问题，提出利用地面特征与相机-地面几何在线标定的Ground-VIO方法，显著提升里程计精度，性能媲美甚至优于立体VIO。
摘要：
单目视觉惯性里程计（VIO）是一种低成本解决方案，能够提供高精度、低漂移的位姿估计。然而，由于车辆场景中动力学受限且缺乏稳定特征，该方案面临挑战。本文提出Ground-VIO，利用地面特征和特定的相机-地面几何关系来增强单目VIO在真实道路环境中的性能。在该方法中，相机-地面几何关系采用以车辆为中心的参数进行建模，并集成到基于优化的VIO框架中。这些参数可在线标定，并通过提供稳定的尺度感知能力同时提高里程计精度。此外，设计了一个专门的视觉前端，通过逆透视映射（IPM）技术稳定地提取和跟踪地面特征。为验证所提方法的有效性，进行了仿真测试和真实世界实验。结果表明，我们的实现能够显著提高单目VIO在车辆场景中的精度，达到与最先进的立体VIO解决方案相当甚至更好的性能。该系统还可用于车辆感知中广泛使用的IPM的自动标定。一个用于地面特征处理的工具包以及实验数据集将开源（https://github.com/GREAT-WHU/gv_tools）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
