计算机视觉
人工智能
扩展卡尔曼滤波器
稳健性(进化)
计算机科学
超声波传感器
机器人
传感器融合
跟踪系统
卡尔曼滤波器
单目视觉
生物化学
化学
物理
声学
基因
作者
Mengmeng Wang,Yong Liu,Daobilige Su,Yufan Liao,Lei Shi,Jinhong Xu,Jaime Valls Miró
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2018-06-01
卷期号:23 (3): 997-1006
被引量:47
标识
DOI:10.1109/tmech.2018.2820172
摘要
Acquiring the accurate three-dimensional (3-D) position of a target person around a robot provides valuable information that is applicable to a wide range of robotic tasks, especially for promoting the intelligent manufacturing processes of industries. This paper presents a real-time robotic 3-D human tracking system that combines a monocular camera with an ultrasonic sensor by an extended Kalman filter (EKF). The proposed system consists of three submodules: a monocular camera sensor tracking module, an ultrasonic sensor tracking module, and the multisensor fusion algorithm. An improved visual tracking algorithm is presented to provide 2-D partial location estimation. The algorithm is designed to overcome severe occlusions, scale variation, target missing, and achieve robust redetection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot, and time of flight is used for the 2-D partial location estimation. EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor, and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both a simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the persuasive performance of the 3-D tracking system in terms of both the accuracy and robustness.
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