In the robot follow-ahead task, a mobile robot is tasked to maintain its relative position in front of a
moving human actor while keeping the actor in sight.
To accomplish this task it is important that the robot understand the full 3D pose of the human (since
the head orientation can be different than the torso) and predict the future human poses so as to plan
accordingly.
The latter task is tricky in a complex environment with junctions and multiple corridors.
In this work, we address the problem of forecasting the full 3D trajectory of a human in such
environments.
Our main insight is to show that one can first predict the 2D trajectory and then estimate the full 3D
trajectory by conditioning the estimator on the predicted 2D trajectory.
With this approach, we achieve results comparable or better than the state-of-the-art three times
faster.
As part of our contribution, we present a new dataset where, in contrast to existing datasets, the human
motion is in a much larger area than a single room.
We also present a complete robot system that integrates our human pose forecasting network on the mobile
robot to enable real-time robot follow-ahead and present results from real-world experiments in multiple
buildings on campus.
