This paper presents a novel method to assess the resilience of the Iterative Closest Point (ICP) algorithm via deep-learning-based attacks on lidar point clouds. For safety-critical applications such as autonomous navigation, ensuring the resilience of algorithms prior to deployments is of utmost importance. The ICP algorithm has become the standard for lidar-based localization. However, the pose estimate it produces can be greatly affected by corruption in the measurements. Corruption can arise from a variety of scenarios such as occlusions, adverse weather, or mechanical issues in the sensor. Unfortunately, the complex and iterative nature of ICP makes assessing its resilience to corruption challenging. While there have been efforts to create challenging datasets and develop simulations to evaluate the resilience of ICP empirically, our method focuses on finding the maximum possible ICP pose error using perturbation-based adversarial attacks. The proposed attack induces significant pose errors on ICP and outperforms baselines more than 88% of the time across a wide range of scenarios. As an example application, we demonstrate that our attack can be used to identify areas on a map where ICP is particularly vulnerable to corruption in the measurements.

翻译：本文提出了一种新颖的方法，通过基于深度学习的激光雷达点云对抗性攻击，评估迭代最近点（ICP）算法的韧性。对于自主导航等安全关键应用，确保算法在部署前的韧性至关重要。ICP算法已成为基于激光雷达定位的标准方法。然而，其产生的位姿估计可能因测量数据损坏而受到显著影响。这种损坏可能源于多种场景，例如遮挡、恶劣天气或传感器机械故障。遗憾的是，ICP算法复杂的迭代特性使得评估其对抗损坏的韧性极具挑战性。尽管已有研究致力于创建具有挑战性的数据集并开发仿真来经验性评估ICP的韧性，但本文方法聚焦于利用基于扰动的对抗性攻击寻找最大可能的ICP位姿误差。所提出的攻击能在ICP上诱导显著的位姿误差，并在广泛场景中超过88%的情况下优于基线方法。作为应用示例，我们展示了该攻击可用于识别地图中ICP对测量损坏特别脆弱的区域。