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Acquiring the accurate 3-D position of a target person around a robotprovides fundamental and valuable information that is applicable to a widerange of robotic tasks, including home service, navigation and entertainment.This paper presents a real-time robotic 3-D human tracking system whichcombines a monocular camera with an ultrasonic sensor by the extended Kalmanfilter (EKF). The proposed system consists of three sub-modules: monocularcamera sensor tracking model, ultrasonic sensor tracking model and multi-sensorfusion. An improved visual tracking algorithm is presented to provide partiallocation estimation (2-D). The algorithm is designed to overcome severeocclusions, scale variation, target missing and achieve robust re-detection.The scale accuracy is further enhanced by the estimated 3-D information. Anultrasonic sensor array is employed to provide the range information from thetarget person to the robot and Gaussian Process Regression is used for partiallocation estimation (2-D). EKF is adopted to sequentially process multiple,heterogeneous measurements arriving in an asynchronous order from the visionsensor and the ultrasonic sensor separately. In the experiments, the proposedtracking system is tested in both simulation platform and actual mobile robotfor various indoor and outdoor scenes. The experimental results show thesuperior performance of the 3-D tracking system in terms of both the accuracyand robustness.

Real-time 3D Human Tracking for Mobile Robots with Multisensors