Longterm Prediction of Motion Trajectories Using Path Homology Clusters In order for robots to share their workspace with people, they need to reason about human motion efficiently. In this work we leverage large datasets of paths in order to infer local models that are able to perform longterm predictions of human motion. Further, since our method is based on simple dynamics, it is conceptually simple to understand and allows one to interpret the predictions produced, as well as to extract a cost function that can be used for planning. The main difference between our method and similar systems, is that we employ a map of the space and translate the motion of groups of paths into vector fields on that map. We test our method on synthetic data and show its performance on the Edinburgh forum pedestrian longterm tracking dataset 1 where we were able to outperform a Gaussian Mixture Model tasked with extracting dynamics from the paths.

Pedestrian Density Prediction for Efficient Mobile Robot Exploration 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 realworld robot deployments in an officelike environment. Experimental results demonstrate that the current method can predict human locations with an accuracy average of 70%.