文献类型：期刊文献

英文题名：Real-time Online UAV Images Mosaic with Robustness to Cumulative Errors

作者：Xia, Xing[1]; Wang, Bei[1]; Li, Jing[2]; Song, Li[1]; Gong, Yi Hang[2]; Deng, Bao Song[2]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, China; [2] National Innovation Institute of Defense Technology, Academy of Military Science Tianjin Artificial Intelligence Innovation Center, Tianjing, China

年份：2020

起止页码：174

外文期刊名：ACM International Conference Proceeding Series

收录：EI(收录号：20202408819973)

语种：英文

外文关键词：Errors - Degrees of freedom (mechanics)

摘要：In this paper, a novel real-time online UAV image mosaic algorithm is proposed to meet the real-time, robustness and accuracy requirements of practical application. First, the algorithm adopts GPS and track planning information to locate the adjacent images. Instead of the 8-DOF (degrees of freedom) homography transformation, the 4-DOF similarity transformation is estimated to more robustly register the UAV images and reduce their perspective errors. Second, a new method of local registration is developed to reduce the cumulative errors and calculation costs. Meanwhile, the real-time mosaic process is used to register images and update the panoramic image. Finally, the effectiveness of the proposed approach is evaluated by a group of experiments on 4K images taken by quadrotor drones. According to the comparison of experiments, the proposed algorithm is robust to cumulative errors and obtains high-quality panorama. Additionally, through the analysis on stitching time and practical application, frame-by-frame mosaic synthesis is online updated in real-time. ? 2020 ACM.