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.

中文翻译：

---

相机和基于 3D 地图的安全城市定位的数据驱动保护级别

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至关重要。许多现有方法使用 GNSS 测量来将保护级别 (PL) 表征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，因此需要考虑额外的传感器模式来确定 PL。在本文中，我们提出了一种通过将相机图像测量与基于 LiDAR 的环境 3D 地图匹配来计算 PL 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异常值加权技术来指定位置误差的高斯混合模型概率分布。从概率分布，我们通过使用数值线搜索方法评估位置误差界限来计算 PL。通过对真实世界数据的实验验证，我们证明了从我们的方法计算的 PL 是城市环境中位置误差的可靠界限。