四旋翼飞行器基于视觉的运动目标检测与跟踪

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	四旋翼飞行器基于视觉的运动目标检测与跟踪
	刘一莎; 姜楠; 王健; 赵忆文
	2014
会议名称	11th World Congress on Intelligent Control and Automation (WCICA 2014)
会议日期	June 29 - July 4, 2014
会议地点	Shenyang, China
关键词	四旋翼飞行器目标跟踪单目视觉混合例子滤波
其他题名	Vision-based moving target detection and tracking using a quadrotor UAV
页码	2358-2363
中文摘要	小型四旋翼飞行器针对地面运动目标的自主检测与跟踪是其实现与运动中的地面机器人进行空地协作的前提与基础。研究中采用一种T 型标识，并将其安装在地面移动机器人的顶部以便于记载单目视觉传感器的快速检测。考虑光照变化及阴影遮挡等因素的影响，采用自适应阈值化和二值图像的拓扑结构分析方法来完成T 型标识的在线检测。由于四旋翼飞行器与地面移动机器人均处于运动状态，为了提高目标跟踪的鲁棒性并兼顾计算效率，提出了利用混合粒子滤波来实现标识目标跟踪的算法。基于马氏准则和最小欧氏距离思想，提出获得粒子最小不确定性观测的方法，从而实现了前景观测与粒子预估观测之间的最优匹配。利用单目视觉系统在室内环境中所获得的实验结果验证了所提方法的有效性。
英文摘要	Autonomous detecting and tracking of mobile ground robots are two fundamental tasks for the cooperation between quadrotor UAVs and UGVs. In our work, a T-shape landmark is installed on the top of a ground robot so that the quadrotor can use onboard monocular camera to perform fast object detection. Considering the effects of shading, shadows and varying lighting condition, the adaptive thresholding algorithm and topology analysis on a binary image are adopted to accomplish online T-shape landmark detection. Since both the quadrotor and the ground robot are in the continuous moving state, a hybrid particle filter algorithm is presented to implement robust object tracking with a low computational cost. To solve the problem of particle measurement matching optimization, a method of achieving least uncertainty measurement based on Mahalanobis rule and minimum Euclidian distance is utilized in this paper. A series of experiment results with monocular vision in an indoor environment show our approach's validity.
收录类别	EI
产权排序	4
会议录	Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
会议录出版者	IEEE
会议录出版地	Piscataway, NJ, USA
语种	中文
ISBN号	978-1-4799-5825-2
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/16928]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	刘一莎,姜楠,王健,等. 四旋翼飞行器基于视觉的运动目标检测与跟踪[C]. 见:11th World Congress on Intelligent Control and Automation (WCICA 2014). Shenyang, China. June 29 - July 4, 2014.