Improved Visual-Inertial Localization for Low-cost Rescue Robots

TL;DR

This paper enhances visual-inertial localization for low-cost rescue robots by detecting and mitigating sensor faults, significantly improving pose accuracy on rugged terrains through novel threshold and dynamic time warping methods.

Contribution

The paper introduces two new fault detection methods integrated into VINS-Mono, improving localization accuracy for low-cost rescue robots on rugged terrain.

Findings

Both methods improve pose estimation accuracy.

Threshold-based method excels with small noise.

DTW-based method performs better with large noise.

Abstract

This paper improves visual-inertial systems to boost the localization accuracy for low-cost rescue robots. When robots traverse on rugged terrain, the performance of pose estimation suffers from big noise on the measurements of the inertial sensors due to ground contact forces, especially for low-cost sensors. Therefore, we propose \textit{Threshold}-based and \textit{Dynamic Time Warping}-based methods to detect abnormal measurements and mitigate such faults. The two methods are embedded into the popular VINS-Mono system to evaluate their performance. Experiments are performed on simulation and real robot data, which show that both methods increase the pose estimation accuracy. Moreover, the \textit{Threshold}-based method performs better when the noise is small and the \textit{Dynamic Time Warping}-based one shows greater potential on large noise.