Efficient Scale-Uniform 3D Visual Coverage Algorithm for UAV Based on Elastic Photogrammetric Constraints

Jianping Zong, Zhongzhi Cao, Qi Chen, Chuanyu Sun, Xiuli Shao, Haifeng Li, Hongpeng Wang

Abstract: Unmanned aerial vehicles equipped with modern vision algorithms are crucial for missions such as reconstruction and target acquisition. However, when deployed in the field, undulating terrain can cause significant fluctuations in image scale and degrade the performance of vision algorithms. Instead of developing specialized image processing schemes with limited adaptability, this paper presents a novel 3D visual coverage algorithm that is compatible with existing generic vision algorithms and maintains a uniform image scale for ground targets. In detail, photogrammetric constraints are initially introduced to generate aerial waypoints, and then the negative effects of valley clustering are addressed. Elastic Photogrammetric Constraints (EPC) are further proposed to eliminate valley clustering effects induced by saddle terrain. The experimental results demonstrate that EPC reduces the traversal path length by up to 37.38 % compared to the previous work, but with a minor trade-off in scale variations.