RealTime DriftFree Path Recording and Replaying Based On Virtual Image Matching For MAVs This paper presents an efficient method to achieve realtime driftfree path replaying for Micro Aerial Vehicle (MAV). The driftfree path replaying method is proposed for precise autonomous path following in GPSdenied environments. In this paper, we present solutions on how to effectively select recording images during flying and drive the MAV to fly automatically to target position. During the path replaying, a strategy named triggered virtual image matching is used for reducing the computational cost and eliminating the drift. Time of image selection is reduced to negligible due to above strategy. Moreover, we use optical flow method for features matching between current image and the virtual image which is precise and faster. Experimental results show that our approach can achieve autonomous returning and precise path following in indoor scene and outdoor scene.

Online Trajectory Generation of a MAV for Chasing a Moving Target in 3D Dense Environments This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment. In contrast to obstaclefree or sparse environments, the chaser should be able to handle collision and occlusion together with flight efficiency. In order to tackle these challenges in realtime, we introduce a metric for target visibility and propose a hierarchical chasing planner. In the first phase, we generate a sequence of waypoints and chasing corridors which ensure safety and optimize visibility. In the following phase, the corridors and waypoints are utilized as constraints and objective respectively in quadratic programming from which we complete a dynamically feasible trajectory for chasing. The proposed algorithm is tested in multiple dense environments. The simulator AutoChaser with full code implementation u0026 GUI can be found in https:github.comicslJeontraj_gen_vis and video is available at https:youtu.be2d3uDlYR_M.