  This paper explores the use of 3D lidar in a physical Human-Robot Interaction
(pHRI) scenario. To achieve the aforementioned, experiments were conducted to
mimic a modern shop-floor environment. Data was collected from a pool of
seventeen participants while performing pre-determined tasks in a shared
workspace with the robot. To demonstrate an end-to-end case; a perception
pipeline was developed that leverages reflectivity, signal, near-infrared, and
point-cloud data from a 3-D lidar. This data is then used to perform safety
based control whilst satisfying the speed and separation monitoring (SSM)
criteria. In order to support the perception pipeline, a state-of-the-art
object detection network was leveraged and fine-tuned by transfer learning. An
analysis is provided along with results of the perception and the safety based
controller. Additionally, this system is compared with the previous work.
