Robust GNSS Denied Localization for UAV Using Particle Filter and Visual Odometry

TL;DR

This paper presents a robust UAV localization method that combines particle filtering with visual odometry, significantly improving accuracy in GNSS-denied environments by using image similarity functions and outperforming existing algorithms.

Contribution

Introduces a novel particle filter localization approach using parametric image similarity functions, enhancing robustness and accuracy in GNSS-denied UAV navigation scenarios.

Findings

Particle filter with logistic similarity surpasses ORB-SLAM2 accuracy by 2.6 times.

The proposed method maintains 70% accuracy with outdated maps.

Image similarity functions significantly impact localization performance.

Abstract

Conventional autonomous Unmanned Air Vehicle (abbr. UAV) autopilot systems use Global Navigation Satellite System (abbr. GNSS) signal for navigation. However, autopilot systems fail to navigate due to lost or jammed GNSS signal. To solve this problem, information from other sensors such as optical sensors are used. Monocular Simultaneous Localization and Mapping algorithms have been developed over the last few years and achieved state-of-the-art accuracy. Also, map matching localization approaches are used for UAV localization relatively to imagery from static maps such as Google Maps. Unfortunately, the accuracy and robustness of these algorithms are very dependent on up-to-date maps. The purpose of this research is to improve the accuracy and robustness of map relative Particle Filter based localization using a downward-facing optical camera mounted on an autonomous aircraft. This research shows how image similarity to likelihood conversion function impacts the results of Particle Filter localization algorithm. Two parametric image similarity to likelihood conversion functions (logistic and rectifying) are proposed. A dataset of simulated aerial imagery is used for experiments. The experiment results are shown, that the Particle Filter localization algorithm using the logistic function was able to surpass the accuracy of state-of-the-art ORB-SLAM2 algorithm by 2.6 times. The algorithm is shown to be able to navigate using up-to-date maps more accurately and with an average decrease of precision by 30% using out-of-date maps.