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.
In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical ... In this paper we present the development and implementation of a patrol robot for indoor environments. First, we propose an indoor patrol strategy by using ultrasonic sensors. According to the gathered range information of the environment, we apply the wall-following strategy to design of patrol rules for determining the navigation commands of the patrol robot. Then we design a steering controller based on the potential field method to drive the patrol robot moving along the wall baselines. In addition, we propose another navigation strategy by using ultrasonic and vision data fusion to improve the accuracy and robustness of the sensing system. We implement a visual navigation controller to successfully steer the patrol robot following the wall and keeping a certain distance to the wall. Service robots intended to interact with people must be able to localize and continuously track their users. A method is described which integrates information from visual and sonar based tracking path- ways while updating hypotheses about the position of the robot's human user. Each tracking method uses information from the other to generate a more robust measure of the user's position, and thus a more robust behavior generation is achieved.