Self-Localization of Outdoor Autonomous Flight Drone Using Point Cloud with AR Marker Information Generated using SfM and ORB-SLAM

Abstract

Achieving practical and full-scale use of drones will require a transition from a first-person view (FPV) flight based on visual radio control to an autonomous wide-area, long-distance flight. However, technology that enables drones to fly autonomously over a wide area and long distances while feeding back positioning in ever-changing real-world environments is yet to be established. We aimed to develop a SLAM system that combines ORB-SLAM and dense point clouds in the environment. The result of an evaluation experiment of the developed SLAM system using a simulator indicated that the estimated self-position was corrected using matching with the dense point cloud, and a system combining ORB-SLAM and the dense point cloud was developed. It was confirmed that a SLAM system combining ORB-SLAM and some dense points in the environment could be developed. We have achieved good results in basic operation trials, demonstrating the potential of both systems for practical use.