深度学习辅助的惯性/视觉/ LiDAR 集成用于 GNSS 具有挑战性的环境
Nader Abdelaziz1,2, Ahmed El-Rabbany1
1Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种集成导航系统,将惯性测量单元 (IMU),LiDAR和摄像头数据融合在一起,用于在GPS中断时准确定位. 该系统大大减少了定位错误,提高了导航可靠性.
科学领域:
- 机器人技术和自主系统
- 导航和定位技术 导航和定位技术
- 传感器融合式传感器
背景情况:
- 全球导航卫星系统 (GNSS) 信号中断给自动驾驶汽车导航带来了重大挑战.
- 在长时间的GNSS拒绝期间,现有系统经常在准确性和可靠性方面扎.
- 准确的定位对于自主系统的安全和高效运行至关重要.
研究的目的:
- 开发和评估一个集成的导航系统,在GNSS中断期间提供可靠的定位.
- 使用扩展卡尔曼波器 (EKF) 将惯性测量单元 (IMU),LiDAR和单眼相机的数据合并.
- 用 LiDAR 深度测量来解决单眼视觉测距中的尺度模糊性.
主要方法:
- 开发一个集成的惯性导航系统 (INS) /单眼视觉同时定位和绘图 (SLAM) 系统.
- 通过扩展卡尔曼波器 (EKF) 进行IMU,LiDAR和单眼相机测量的融合.
- 利用LiDAR深度数据来解决单眼视觉测距中固有的尺度模糊性.
主要成果:
- 实现了横向平均平方根误差 (RMSE) 的平均减少,水平约80%,垂直约92%.
- 与INS/单眼视觉SLAM/LiDAR SLAM集成相比,已经证明了卓越的性能.
- 在使用KITTI和Leddar PixSet数据集的各种驾驶场景中验证了系统性能.
结论:
- 拟议的综合导航系统在GNSS信号中断时提供高度准确和可靠的定位.
- 融合战略有效地利用多传感器数据,克服单个传感器的局限性.
- 这种方法显著提高了自主导航系统的稳定性.
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