Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses

In this work a multi-robot system is presented for people detection and tracking in automated warehouses. Each Automated Guided Vehicle (AGV) is equipped with multiple RGB cameras that can track the workers' current locations on the floor thanks to a neural network that provides human pose estimation. Based on the local perception of the environment each AGV can exploit information about the tracked people for self-motion planning or collision avoidance. Additionally, data collected from each robot contributes to a global people detection and tracking system. A warehouse central management software fuses information received from all AGVs into a map of the current locations of workers. The estimated locations of workers are sent back to the AGVs to prevent potential collision. The proposed method is based on two-level hierarchy of Kalman filters. Experiments performed in a real warehouse show the viability of the proposed approach.