Robust Autonomous Navigation of Unmanned Aerial Vehicles (UAVs) for Warehouses’ Inventory Application

Abstract

The inventory inspection using autonomous unmanned aerial vehicles (UAVs) is beneficial in terms of cost, time, and safety of human workers. However, in typical warehouses, it is very challenging for the autonomous UAVs to do inventory task motions safely because aisles are narrow and long, and the illumination is poor. Prior autonomous UAVs are not suitable for such environments, since they suffer from either localization methods prone to disturbance, drift and outliers, or expensive sensors. We present a low-cost sensing system with an extended Kalman filter (EKF)-based multi-sensor fusion framework to achieve practical autonomous navigation of UAVs in warehouse environments. To overcome the inherent drift, outliers, and disturbance problems of naïve UAV localization methods, we suggest 1) exploiting component test of Mahalanobis norm to reject outliers efficiently, 2) introducing pseudo-covariance to incorporate a visual SLAM algorithm, and 3) recognizing floor lanes to get absolute information–as robust data fusion methods. Exemplar results are provided to demonstrate the effectiveness of the methods. The proposed system has been successfully implemented for diverse cyclic inventory inspection tasks in a materials warehouse.