Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm

2024-03-08

arXiv.org

AI Summary

Evaluating the robustness of the Iterative Closest Point (ICP) algorithm under degradation conditions—such as occlusion, adverse weather, or sensor failure—is challenging in safety-critical LiDAR-based localization. Method: This paper introduces, for the first time, differentiable adversarial attacks into ICP resilience analysis. We propose an end-to-end learnable perturbation framework that employs gradient-guided local geometric perturbations to maximize pose estimation error. By integrating deep neural networks with differentiable ICP modeling, our approach overcomes the limitations of hand-crafted adversarial samples and empirical testing. Results: Experiments demonstrate an 88% higher attack success rate across diverse scenarios compared to baseline methods. Moreover, the attack precisely identifies highly vulnerable regions within prior maps, enabling quantifiable and interpretable assessment. This provides actionable insights for designing robust localization algorithms and ensuring safe deployment in real-world autonomous systems.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Assessing ICP algorithm resilience to worst-case adversarial attacks
• Identifying map locations vulnerable to lidar point cloud corruption
• Enabling safer autonomous navigation paths through pre-deployment analysis

Innovation

• Learning-based adversarial attack for ICP resilience
• Identifies map locations vulnerable to corruption
• Perturbation-based method outperforms baselines significantly