In this report we present our first prototype of the perception pipeline developed for the CROWDBOT project. Currently, the focus of the perception pipeline is on detecting and tracking pedestrians in low to medium density scenarios using RGB-D cameras and 2D LiDAR sensors. We begin with reviewing the major detection and tracking methods used in
The CrowdBot project’s crowd simulators perform two essential roles for the safe navigation of robots in populated environments: Crowd simulation for short-term prediction of the evolution of the situation of people in the vicinity of the robot. Crowd simulation for testing and evaluating the navigation functions of a robot in a densely populated environment. This
This report details the mapping, localisation and path planning solutions developed for the CROWDBOT project between months M1 to M20. Each of the three technical components have been designed with the explicit goal of achieving robot navigation in crowded environments, where many existing methods struggle due to the high degree of dynamic motion around the
CrowdBot Excom was hosted at INRIA Paris in September, 2019.
Authors: Salaris, Paolo; Cognetti, Marco; Spica, Riccardo; Robuffo Giordano, Paolo
Authors: Paez-Granados, Diego Felipe; Gonon, David; Salvini, Pericle; Billard, Aude
Authors: Garcia, Fernando ; Mazel, Alexandre; Cruz-Maya, Arturo – SoftBank Robotics Europe
Authors: Amirian, Javad; van Toll, Wouter; Hayet, Jean-Bernard; Pettré, Julien
CrowdBot All-hands meeting was hosted at ETH Zürich in June, 2019.
Authors: Alessandra Rossi, Fernando Garcia, Arturo Cruz Maya, Kerstin Dautenhahn, Kheng Lee Koay, Michael L. Walters, and Amit K. Pandey