An Automatic and Accurate Method for Marking Ground Control Points in Unmanned Aerial Vehicle Photogrammetry

Abstract

Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.

AHF: An Automatic and Universal Image Preprocessing Algorithm for Circular-Coded Targets Identification in Close-Range Photogrammetry under Complex Illumination Conditions

“…AM is a tool for three-dimensional reconstruction, visualization, measurement, and mapping tasks and is currently a highly competitive photogrammetric software on the market [29,40]. The workflow of TI [33] is first to filter out CCTs based on criteria such as ellipse circumference and roundness and then use ellipse fitting to locate the center.…”

“…Finally, decoding is performed by searching for a coded band through the center point for scanning and decoding. Generally, the coded band is directly located based on the geometric structure of the CCT, while Liu et al [14] and Kong et al [40] searched for the coded band through the grayscale averaging method, which is helpful for identification under flat viewing angles. Decoding operations are based on grayscale gradient methods [41][42][43][44][45][46].…”

An accurate and robust visual-inertial positioning method

“…Zhong et al designed artificial landmarks patterned with a set of concentric circular rings in black and white and used a bilayer recognition algorithm to improve the positioning robustness [17]. Kong et al presented a novel centripetal circular coded target to realize an automatic and accurate approach for marking ground control points [18]. However, these passive landmarks are easily interfered by illumination, which limits their applications in complex working environments.…”