Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles

This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target's altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target's two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments.

Automated summary

This paper presents a framework for geolocating ground-based moving targets using images from dual unmanned aerial vehicles (UAVs). The framework eliminates the need for accurate navigation state sensors or assumptions about the target's altitude, and instead utilizes dual UAVs equipped with low-quality sensors. The proposed framework includes a corresponding-point-matching method using historical measurement data, an altitude estimation method based on multiview geometry, and a process to estimate the yaw-angle measurement biases of the UAVs. Simulation and actual flight experiments confirm the effectiveness and practicality of the framework.