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雷达和视觉里程计集成系统辅助 GNSS 拒止环境下的无人机导航。

Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment.

机构信息

Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

Department of Electrical Engineering, Port-Said University, Port Said 42523, Egypt.

出版信息

Sensors (Basel). 2018 Aug 23;18(9):2776. doi: 10.3390/s18092776.

Abstract

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system's ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

摘要

无人机在消防、救援等广泛领域的应用变得越来越重要。然而,在具有挑战性的环境中飞行时,可靠的全球导航卫星系统 (GNSS) 测量并不能保证一直可用,惯性导航系统 (INS) 的导航解决方案也会急剧恶化。虽然提出了不同的辅助传感器,如相机,以减少这些漂移误差的影响,但使用这些技术的定位精度仍然受到一些挑战的影响,例如缺乏观测特征、不一致的匹配、光照和环境条件。本文提出了一种基于雷达测距 (RO) 和增强视觉里程计 (VO) 的无人机 (UAV) 组合导航系统,用于处理 GNSS 拒止环境中的此类挑战,因为雷达不受这些问题的影响。通过扩展卡尔曼滤波器 (EKF) 将来自 RO 和增强 VO 的车辆估计前进速度与惯性测量单元 (IMU)、气压计和磁力计测量值融合,以在 GNSS 信号中断期间提高导航精度。RO 和 VO 集成到一个集成系统中,以帮助克服它们的局限性,因为在飞越不平坦地形时,RO 测量会受到影响。因此,在这种情况下,VO 的集成是很重要的。实验结果表明,所提出的系统在 GNSS 信号中断期间能够显著提高 3D 定位精度。

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