Visual Measurement Integrity Monitoring for UAV Localization

TL;DR

This paper introduces a novel integrity monitoring approach for UAV visual localization inspired by RAIM, providing reliable error bounds and validation on real datasets to enhance safety in UAV missions.

Contribution

It proposes a new RAIM-inspired method to assess and compute the protection level of visual localization accuracy for UAVs, addressing a gap in existing approaches.

Findings

Protection level offers more reliable bounds than $3\sigma$ method.

Method validated on EuRoC dataset with improved safety assurances.

Quantitative metric evaluates the performance of error bounds.

Abstract

Unmanned aerial vehicles (UAVs) have increasingly been adopted for safety, security, and rescue missions, for which they need precise and reliable pose estimates relative to their environment. To ensure mission safety when relying on visual perception, it is essential to have an approach to assess the integrity of the visual localization solution. However, to the best of our knowledge, such an approach does not exist for optimization-based visual localization. Receiver autonomous integrity monitoring (RAIM) has been widely used in global navigation satellite systems (GNSS) applications such as automated aircraft landing. In this paper, we propose a novel approach inspired by RAIM to monitor the integrity of optimization-based visual localization and calculate the protection level of a state estimate, i.e. the largest possible translational error in each direction. We also propose a metric that quantitatively evaluates the performance of the error bounds. Finally, we validate the protection level using the EuRoC dataset and demonstrate that the proposed protection level provides a significantly more reliable bound than the commonly used $3\sigma$ method.