  Acquiring the accurate 3-D position of a target person around a robot
provides fundamental and valuable information that is applicable to a wide
range of robotic tasks, including home service, navigation and entertainment.
This paper presents a real-time robotic 3-D human tracking system which
combines a monocular camera with an ultrasonic sensor by the extended Kalman
filter (EKF). The proposed system consists of three sub-modules: monocular
camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor
fusion. An improved visual tracking algorithm is presented to provide partial
location estimation (2-D). The algorithm is designed to overcome severe
occlusions, scale variation, target missing and achieve robust re-detection.
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 Gaussian Process Regression is used for partial
location estimation (2-D). 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 simulation platform and actual mobile robot
for various indoor and outdoor scenes. The experimental results show the
superior performance of the 3-D tracking system in terms of both the accuracy
and robustness.
