Digital Video Stabilization Method Based on Periodic Jitters of Airborne Vision of Large Flapping Wing Robots

Large-scale flapping wing robots (FWRs) with airborne vision have important applications in visual navigation, aerial surveying, fire warning and power-line inspection. However, airborne vision and its videos suffer from strong jitters due to periodic wing flapping, which lowers the success rate of detection and measurement precision. In this paper, a robust digital video stabilization (DVS) method based on periodic jitters is proposed to provide continuous stable monitoring video without pan-tilt camera assistance. First, the periodic motion model of the FWR is established for video jitter analysis. Second, jitter frequencies in different flight states are estimated by continuous jitter acceleration. Then, feature trajectories generated from the video are adjusted adaptively for jitter frequency consistency and smoothed individually by the sampling-interpolation-averaging strategy, including the short trajectories. The stabilized video is generated by guidance from the original and smoothed trajectories. Finally, the proposed method is tested in outdoor flights with a 2.2-meter wingspan FWR and is found to outperform traditional, commercial, and deep learning DVS methods in terms of stability and robustness in various scenes and flight states.