Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors
This addresses safety and efficiency in human-robot collaboration by enabling real-time human detection, though it appears incremental as it builds on existing sensor and tracking methods.
The paper tackles the problem of detecting and tracking human positions in a robot's workspace using on-robot time-of-flight laser sensors, achieving this through a neural network that estimates partial human pose from sparse 3-D point clouds and a particle filter for unreliable data.
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.