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 large and deterministic faults. This paper introduces a closed-form formula approximating the pose error resulting from an ICP algorithm when subjected to the most detrimental adverse measurements. 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：本文提出一种建模迭代最近点（ICP）算法在存在测量异常时鲁棒性的方法。在自动驾驶场景中，认证定位过程的安全性构成重大挑战。当机器人在复杂环境中运行时，各类噪声会对测量产生影响。传统上，此类噪声被假定服从零均值高斯分布。然而，这一假设在众多场景中均不成立，包括恶劣天气条件、动态障碍物造成的遮挡，或地图长期变化等情形。在这些情况下，测量值反而会受到大尺度确定性故障的影响。本文提出一个闭合公式，可近似计算ICP算法在遭受最不利异常测量时产生的位姿误差。基于该公式，我们开发出一种度量方法，用于认证并精确定位环境中当测量值存在故障时机器人更易发生定位失效的特定区域。