Title: Point-Cloud Fast Filter for People Detection with Indoor Service Robots
Authors: Carlos Medina-Sánchez, Jesús Capitán, Matteo Zella and Pedro José Marrón
Conference: 2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Date: November 9-11, 2020
Abstract: Enabling robots to effectively detect people in their surrounding is essential to support operation in social environments. Due to the variety of robotic platforms and sensors available, as well as the constraints imposed on the use of cameras in some scenarios, it is essential to identify solutions able to work across different perception technologies. At the same time, solutions should not impose strong constraints on computing and memory resources, thus allowing robots to perform also complex tasks without affecting their reactiveness. To achieve such a goal, this article introduces Point-Cloud Fast Filter for People Detection (PFF-PED). The algorithm detects people by processing effectively point-cloud data, independently of the specific 3D sensor employed. By focusing on the most informative observations, PFF-PED is able to improve both accuracy and processing times compared to existing 2D and 3D solutions in our initial experimentation in indoor scenarios.