Attribute estimation using pedestrian clothing and autonomous mobile robot navigation based on it

This paper proposes a new method for estimating the attributes of a pedestrian, e.g. an office worker or a student, from the pedestrian's clothing and, based on the estimation results, a method for efficiently navigating an autonomous mobile robot while safely avoiding pedestrians. The robot can estimate the organization the pedestrians belong to by learning in advance the clothing of pedestrians that frequently appear in the city in which the is travelling, e.g. the uniforms of certain high school students. Furthermore, the robot is given the direction in which the person in that organization is likely to be heading at the current time, so it uses this information to estimate the future walking trajectory of that pedestrian and design a running path that is less likely to prevent that pedestrian from walking. We use the artificial potential method to design the robot's running trajectory. Even if pedestrians with different attributes are detected in the robot's running path, our proposed robot can efficiently reach its destination while avoiding those pedestrians appropriately in real time. The effectiveness of the proposed method was confirmed by computer simulations and subjects' experiments with our prototype robot.