Google Map Aided Visual Navigation for UAVs in GPS-denied Environment

TL;DR

This paper introduces a GPS-independent UAV navigation framework using Google Maps, combining geo-referenced localization, optical flow, and particle filtering to achieve drift-free positioning in GPS-denied environments.

Contribution

It presents a novel integration of Google Maps with UAV navigation, utilizing optical flow and particle filtering for accurate localization without loop closures.

Findings

Eliminates drift in dead-reckoning navigation.

Achieves small localization errors in aerial tests.

Demonstrates effectiveness as a GPS supplement.

Abstract

We propose a framework for Google Map aided UAV navigation in GPS-denied environment. Geo-referenced navigation provides drift-free localization and does not require loop closures. The UAV position is initialized via correlation, which is simple and efficient. We then use optical flow to predict its position in subsequent frames. During pose tracking, we obtain inter-frame translation either by motion field or homography decomposition, and we use HOG features for registration on Google Map. We employ particle filter to conduct a coarse to fine search to localize the UAV. Offline test using aerial images collected by our quadrotor platform shows promising results as our approach eliminates the drift in dead-reckoning, and the small localization error indicates the superiority of our approach as a supplement to GPS.