基于摄像头和3D地图的安全城市定位的数据驱动保护级别

NAVIGATION Pub Date : 2021-09-10 DOI:10.1002/navi.445
Shubh Gupta, Grace Gao
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引用次数: 0

摘要

可靠地评估估计车辆位置的误差对于确保车辆在城市环境中的安全是不可或缺的。许多现有的方法使用GNSS测量来表征保护水平(PLs)作为位置误差的概率上限。然而,GNSS信号在城市环境中可能会被反射或阻挡,因此需要考虑其他传感器模式来确定PLs。在本文中,我们提出了一种通过将相机图像测量值与基于lidar的环境3D地图相匹配来计算PLs的方法。我们使用基于深度神经网络的数据驱动模型和统计离群值加权技术指定位置误差的高斯混合模型概率分布。从概率分布出发,利用数值寻线法计算位置误差界,从而计算出PL。通过实际数据的实验验证,我们证明了用我们的方法计算的PLs是城市环境中位置误差的可靠边界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven protection levels for camera and 3D map-based safe urban localization
Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural-network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.
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