Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots

The capability of following a person is crucial in service-oriented robots for human assistance and cooperation. Though a vast variety of following systems exist, they lack robustness against dynamic changes of the environment and relocating to continue following a lost target. Here we present a robust human following system that has the extendability to commercial service robot platforms having a RGB-D camera. The proposed framework integrates deep learning methods for perception and variational Bayesian techniques for trajectory prediction. Deep learning modules enable robots to accompany a person by detecting the target, learning the target and following while avoiding collision within the dynamic home environment. The variational Bayesian techniques robustly predict the trajectory of the target by empowering the following ability of the robot when target is lost. We experimentally demonstrate the capability of the deep Bayesian trajectory prediction method on real-time usage, following abilities, collision avoidance and trajectory prediction of the system. The proposed system was deployed at the RoboCup@Home 2017 Social Standard Platform League and successfully demonstrated its robust functions and smooth person following capability resulting in winning the 1st place.