Pedestrian Density Prediction for Efficient Mobile Robot Exploration

Abstract

We present a method to predict humans in unexplored map areas given limited observations of the environment. We used a geometric representation of the environment based on cost maps and semantic room categorization. Human density distributions were generated using a human tracker based on LiDAR data recorded by a mobile robot. A Gaussian Process (GP) regression model was created to predict human density in surrounding unobserved map locations. GP prediction performance was evaluated on density data recorded in a series of ten simulations of 25 persons in an office setting, and in real-world robot deployments in an office-like environment. Experimental results demonstrate that the current method can predict human locations with an accuracy average of 70%.