  In this paper, we propose a simple methodology to detect the partial pose of
a human occupying the manipulator work-space using only on-robot
time--of--flight laser ranging sensors. The sensors are affixed on each link of
the robot in a circular array fashion where each array possesses sixteen single
unit laser ranging lidar(s). The detection is performed by leveraging an
artificial neural network which takes a highly sparse 3-D point cloud input to
produce an estimate of the partial pose which is the ground projection frame of
the human footprint. We also present a particle filter based approach to the
tracking problem when the input data is unreliable. Ultimately, the simulation
results are presented and analyzed.
