Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localization algorithms must be designed. This work considers scenarios where the sensor data comes from images captured by an inexpensive camera mounted on the vehicle and where the vehicle contains a fine global map. Such localization algorithms typically involve finding the section in the global map that best matches the captured image. In harsh environments, both the global map and the captured image can be noisy. Because of physical constraints on camera placement, the image captured by the camera can be viewed as a noisy perspective transformed version of the road in the global map. Thus, an optimal algorithm should take into account the unequal noise power in various regions of the captured image, and the intrinsic uncertainty in the global map due to environmental variations. This article briefly reviews two matching methods: (i) standard inner product (SIP) and (ii) normalized mutual information (NMI). It then proposes novel and principled modifications to improve the performance of these algorithms significantly in noisy environments. These enhancements are inspired by the physical constraints associated with autonomous vehicles. They are grounded in statistical signal processing and, in some context, are provably better. Numerical simulations demonstrate the effectiveness of such modifications.

翻译：鲁棒且精细的定位算法对于自动驾驶至关重要。为了实现此类车辆的规模化生产，必须设计经济实惠的传感解决方案与可靠的定位算法。本研究考虑以下场景：传感器数据来源于安装在车辆上的低成本摄像头所捕获的图像，且车辆配备精细的全局地图。此类定位算法通常涉及在全局地图中寻找与捕获图像最匹配的路段。在恶劣环境下，全局地图与捕获图像均可能受到噪声干扰。由于摄像头安装的物理限制，摄像头捕获的图像可视为全局地图中道路的含噪透视变换版本。因此，最优算法应考虑捕获图像不同区域的不均衡噪声功率，以及环境变化导致的全局地图固有不确定性。本文简要回顾两种匹配方法：（i）标准内积法（SIP）与（ii）归一化互信息法（NMI），随后提出新颖且基于原理的改进方案，以显著提升这些算法在噪声环境中的性能。这些改进受到自动驾驶车辆物理约束的启发，其理论基础立足于统计信号处理，并在特定条件下可证明具有更优性能。数值仿真验证了所提改进方案的有效性。