Real-time active detection of targets and path planning using UAVs

Abstract

This article proposes a new method that enables Unmanned Aerial Vehicles (UAVs) to actively find targets and shoot photographs of them in an unknown environment, while successfully avoiding surrounding obstacles and planning optimize routes. Owing to the limited computing ability on the UAVs, we obtained the point cloud data of surrounding objects, and selected the best segmentation method of the point cloud to perform real-time semantic segmentation on the collected point cloud data. The point cloud data with semantic attributes were merged into voxels. We reconstruct the real-time distance and angle between the surface of obstacles and the surrounding obstacles through Euclidean Signed Distance Fields (ESDFs), and adjust the gimbal angle and focal length of UAVs and use the two-dimensional image recognition to shoot the photographs of the target precisely. Considering the increasing scale of UAVs power inspections, we can improve the efficiency of fine inspections of power transmission lines by using the method we proposed.