In this paper, we propose a way to model the resilience of the Iterative Closest Point (ICP) algorithm in the presence of corrupted measurements. In the context of autonomous vehicles, certifying the safety of the localization process poses a significant challenge. As robots evolve in a complex world, various types of noise can impact the measurements. Conventionally, this noise has been assumed to be distributed according to a zero-mean Gaussian distribution. However, this assumption does not hold in numerous scenarios, including adverse weather conditions, occlusions caused by dynamic obstacles, or long-term changes in the map. In these cases, the measurements are instead affected by a large, deterministic fault. This paper introduces a closed-form formula approximating the highest pose error caused by corrupted measurements using the ICP algorithm. Using this formula, we develop a metric to certify and pinpoint specific regions within the environment where the robot is more vulnerable to localization failures in the presence of faults in the measurements.

翻译：本文提出了一种对迭代最近点（ICP）算法在存在测量值被篡改情况下鲁棒性的建模方法。在自动驾驶汽车背景下，认证定位过程的安全性是一项重大挑战。由于机器人在复杂环境中运行，各种类型的噪声都可能影响测量值。传统上，这些噪声被假设服从零均值高斯分布。然而，这一假设在包括恶劣天气条件、动态障碍物造成的遮挡或地图长期变化等多种场景中并不成立。在这些情况下，测量值实际上受到较大的确定性故障的影响。本文引入了一个闭式公式，用于近似计算由被篡改测量值导致的最高位姿误差。利用该公式，我们开发了一种指标，用于认证并精确定位环境中在测量值存在故障时机器人更易发生定位故障的具体区域。