A Rapid UAV Image Georeference Algorithm Developed for Emergency Response

Abstract

The image collection system based on the unmanned aerial vehicle plays an important role in the postearthquake response and disaster investigation. In the postearthquake response period, for hundreds of image stitching or 3D model reconstruction, the traditional UAV image processing methods may take one or several hours, which need to be improved on the efficiency. To solve this problem, the UAV image rapid georeference method for postearthquake is proposed in this paper. Firstly, we discuss the rapid georeference model of UAV images and then adopt the world file designed and developed by ESRI to organize the georeferenced image data. Next, the direct georeference method based on the position and attitude data collected by the autopilot system is employed to compute the upper-left corner coordinates of the georeferenced images. For the differences of image rotation manners between the rapid georeference model and the world file, the rapid georeference error compensation model from the image rotation is considered in this paper. Finally, feature extraction and feature matching for UAV images and referenced image are used to improve the accuracy of the position parameters in the world file, which will reduce the systematic error of the georeferenced images. We use the UAV images collected from Danling County and Beichuan County, Sichuan Province, to implement the rapid georeference experiments employing different types of UAV. All the images are georeferenced within three minutes. The results show that the algorithm proposed in this paper satisfies the time and accuracy requirements of postearthquake response, which has an important application value.

Development of Georeferenced 3D Point Cloud in GPS Denied Environments Using Backpack Laser Scanning System

“…The developed models are usually referred in local scanner frames, and some research works have provided solutions to represent the generated model in a global coordinated system [11]. Another research group has transformed the local image coordinates into the Georeferenced coordinates for the emergency assistances [12].…”

Minimum Time Search in Real-World Scenarios Using Multiple UAVs with Onboard Orientable Cameras

“…) by (1) incorporating the current UAVs location and sensor poses 1: and the last sensor measurements 1: with the likelihood functions ( | , ), and by (2) displacing and redistributing the target location probability with the target motion model ( | − ). Besides, (1) assumes that the sensors provide measurements only at time steps that are multiple of and its normalization factor is used to ensure that ∑ ∈ Ω ( ) = 1.…”

“…The strong research interest in UAVs trajectory planning takes advantage of the UAVs capabilities to perform different types of military and civil missions, such as georeference [1], wildlife monitoring [2], or target tracking [3]. Moreover, the current developments of their onboard sensorial systems make them also ideal for performing risky longendurance reconnaissance and surveillance operations.…”

“…It is calculable with (2)-(4), setting̃( ) to ( 0 ) for the initial case ( = 0). The first equation is similar to RBF equation (1), but as it lacks the normalization term and all measurements 0: 1: are nondetection, it obtains the "unnormalized belief" ( ) assuming that the onboard sensors do not detect the target from the UAVs trajectories and sensor poses in 0:…”

Multi-Temporal Image Co-Registration of Uav Blocks: A Comparison of Different Approaches

“…can make it hard to find well distributed matches or cause matching errors. In (Wang et al, 2018) the importance of a fast, not too computationally demanding, co-registration procedure for emergency response is stressed. In (Aicardi et al, 2016) an automatic procedure selects a set of anchor (not changing) images that act as reference for epoch-to-epoch registration using SIFT (Lowe, 1999).…”