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

arXiv: https://arxiv.org/abs/1703.10125 · HTML: https://mlsanity.com/paper/1703.10125/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [Long-range UAV Thermal Geo-localization with Satellite Imagery](https://mlsanity.com/paper/2306.02994/) (2023)
- [STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite Imagery](https://mlsanity.com/paper/2405.20470/) (2024)
- [Jointly Optimized Global-Local Visual Localization of UAVs](https://mlsanity.com/paper/2310.08082/) (2023)
- [UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localization](https://mlsanity.com/paper/2502.01035/) (2025)
- [Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching](https://mlsanity.com/paper/2107.00689/) (2021)
- [Vision-based localization methods under GPS-denied conditions](https://mlsanity.com/paper/2211.11988/) (2022)

## Abstract (arXiv)

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.