Prepared for the Worst: A Learning-Based Adversarial Attack for
  Resilience Analysis of the ICP Algorithm
Ziyu Zhang Johann Laconte Daniil Lisus Timothy D. Barfoot
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.