Map-Aware Human Pose Prediction for Robot Follow-Ahead
CoRR(2024)
Abstract
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 future human poses so as to plan accordingly. This
prediction task is especially 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 methods 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. Our project page, including
supplementary material and videos, can be found at:
https://qingyuan-jiang.github.io/iros2024_poseForecasting/
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