Toward Certifying Maps for Safe Registration-based Localization Under Adverse Conditions

TL;DR

This paper develops a method to certify the safety of ICP-based localization in autonomous vehicles under adverse conditions by modeling measurement faults and identifying vulnerable regions.

Contribution

It introduces a closed-form formula to approximate pose error under worst-case measurement faults and a metric to identify environment regions prone to localization failures.

Findings

Derived a formula for pose error with corrupted measurements

Created a metric to identify vulnerable environment regions

Validated the approach in adverse weather scenarios

Abstract

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.