  For physical human-robot interactions (pHRI), a robot needs to estimate the
accurate body pose of a target person. However, in these pHRI scenarios, the
robot cannot fully observe the target person's body with equipped cameras
because the target person must be close to the robot for physical interaction.
This close distance leads to severe truncation and occlusions and thus results
in poor accuracy of human pose estimation. For better accuracy in this
challenging environment, we propose an active measurement and sensor fusion
framework of the equipped cameras with touch and ranging sensors such as 2D
LiDAR. Touch and ranging sensor measurements are sparse but reliable and
informative cues for localizing human body parts. In our active measurement
process, camera viewpoints and sensor placements are dynamically optimized to
measure body parts with higher estimation uncertainty, which is closely related
to truncation or occlusion. In our sensor fusion process, assuming that the
measurements of touch and ranging sensors are more reliable than the
camera-based estimations, we fuse the sensor measurements to the camera-based
estimated pose by aligning the estimated pose towards the measured points. Our
proposed method outperformed previous methods on the standard occlusion
benchmark with simulated active measurement. Furthermore, our method reliably
estimated human poses using a real robot, even with practical constraints such
as occlusion by blankets.
