Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors

TL;DR

This paper introduces a method using on-robot laser sensors and neural networks to detect and track human partial poses in robot workspaces, enhancing safety and interaction capabilities.

Contribution

It presents a novel approach combining laser ranging sensors, neural networks, and particle filters for human pose detection and tracking on robotic manipulators.

Findings

Effective human pose detection with sparse 3D point clouds

Robust tracking using particle filter under unreliable data

Simulation results demonstrate system viability

Abstract

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.