Kinect sensors are able to achieve considerable skeleton tracking performance in a convenient and low-cost manner. However, Kinect sensors often generate poor skeleton poses due to self-occlusion, which is a common problem among most vision-based sensing systems. A simple way to solve this problem is to use multiple Kinect sensors in a workspace and combine the measurements from the different sensors. However, this method creates a new issue known as the data fusion problem. In this research, we developed a human skeleton tracking system using the Kalman filter framework, in which multiple Kinect sensors are used to correct inaccurate tracking data from a single Kinect sensor. Our contribution is to propose a method to determine the reliability of each tracked 3D position of a joint and then combine multiple observations based on measurement confidence. We evaluate the proposed approach by comparison with the ground truth obtained using a commercial marker-based motion-capture system.

This article introduces a novel confidence random tree-based sampling path planning algorithm for mobile service robots operating in real environments. The algorithm is time efficient, can accommodate narrow corridors, enumerates possible solutions, and minimizes the cost of the path. These benefits are realized by incorporating notable approaches from other existing path planning algorithms into the proposed algorithm. During path selection, the algorithm considers the length and safety of each path via a sampling and rejection method. The algorithm operates as follows. First, the confidence of a path is computed based on the clearance required to ensure the safety of the robot, where the clearance is defined as the distance between the path and the closest obstacle. Then, the sampling method generates a tree graph in which the edge lengths are controlled by the confidence. In a low confidence space, such as a narrow corridor, the corresponding graph has denser samples with short edges while in a high confidence space, the samples are widely spaced with longer edges. Finally, a rejection method is employed to ensure a reasonably short computation time by optimizing the sample density by rejecting unnecessary samples. The performance of the proposed algorithm is validated by comparing the experimental results to those of several commonly used algorithms.

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