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Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments

机译：在具有挑战性的GNSS环境中单目摄像头/ IMU / GNSS集成可用于地面车辆导航

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摘要

Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.
机译：低成本的基于MEMS的IMU，摄像机和便携式GNSS设备在商业上可用于汽车应用，并且一些制造商已经将此类设施集成到他们的车辆系统中。 GNSS为全球用户提供定位，导航和计时解决方案。但是，信号衰减，反射或阻塞可能会导致定位困难。与GNSS相反，独立于电磁波接收的通用IMU可以计算高带宽导航解决方案，但是自包含IMU的输出会随着时间的推移积累误差。此外，摄像机在导航社区中，尤其是在具有挑战性的GNSS环境中，还具有作为备用传感器的巨大潜力，并已成为车辆中的选装件。为了利用这些现有的机载技术在恶劣的环境中进行地面车辆导航，本文基于扩展的卡尔曼滤波器（EKF）开发了集成的摄像头/ IMU / GNSS系统。我们建议的集成体系结构是使用在运营交通环境中收集的实时数据集进行检查的。实验结果表明，在具有稀疏GNSS观测值的挑战性环境中，所提出的集成系统可以提供准确的估计值，并且可能胜过紧密耦合的GNSS / IMU集成。

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