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This paper addresses the issue of how high-speed robots may move among humans such that the robots complete their tasks efficiently while the humans in the environment feel safe and comfortable. It describes the Segway robotic platform used for this research and then discusses the three primary research areas needed to develop the human-aware motion planner. First, it is necessary to conduct experiments with humans to develop human aware velocity constraints as a function of the distance of the robot from a human. Next, these velocity constraints must be used to plan the robot motion in real time. Finally, practical implementation of this motion planner requires the ability to robustly detect humans using the available vision sensors. The approach taken to each of these problems is described in this paper along with preliminary results.

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This paper presents an efficient algorithm to achieve accurate subpixel matchings for calculating correspondences between stereo images based on a path-based matching algorithm. Compared with point-by-point stereo-matching algorithms, path-based algorithms resolve local ambiguities by maximizing the cross correlation (or other measurements) along a path, which can be implemented efficiently using dynamic programming. An effect of the global matching criterion is that cross correlations at all pixels contribute to the criterion; since cross correlation can change significantly even with subpixel changes, to achieve subpixel accuracy, it is no longer sufficient to first find the path that maximizes the criterion at integer pixel locations and then refine to subpixel accuracy. In this paper, by writing bilinear interpolation using integral images, we show that cross correlations at all subpixel locations can be computed efficiently and, thus, lead to a subpixel accuracy path-based matching algorithm. Our results show the feasibility of the method and illustrate significant improvement over existing path-based matching methods.

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