For robots to be able to naturally co-habitate human spaces, as well as to interact with single humans or groups of humans, they should be able to navigate in ways that are human-friendly, and appropriate to human spatial interaction social norms, such as keeping personal spaces. For that purpose, we are developing a special theory which extends path planning, which we call social path plan, which allows humans or groups as obstacles or goals. In order to provide tuning for our simulation results, we are acquiring a natural human interaction dataset, through measurements from multiple laser ranging sensors positioned at a cross-roads indoor space. We thus describe our system consisting of spatial and temporal alignment algorithms for multiple laser sensors, as well as foreground detection, sensor data fusion, segmentation, tracking, two-legged position and pose estimation, and event detection. The method presented can be easily extended to larger spaces and applied for many other application domains beyond our main goal of learning optimal spatial interaction behaviours for human-robot interaction.
