Optimizing UAV-Based Inventory Detection and Quantification in Industrial Warehouses: A LiDAR-Driven Approach

Abstract

The advancement of technology has brought about a revolution in industrial operations, where specialized tools play a crucial role in enhancing efficiency. This study delves into the significant impact of the logistics department in global industries and proposes an innovative solution for inventory detection and recognition using unmanned aerial vehicles (UAVs) equipped with LiDAR technology. Unlike existing research that often involves intricate hardware systems and algorithms leading to increased costs and computational demands, our research focuses on streamlining the inventory detection process by utilizing a LiDAR data and an algorithmic approach that minimizes the time of extensive counting process into the warehouse to quantify the pallets existing. The proposed methodology entails a custom-made quadcopter equipped with a single-beam and high-frequency LiDAR range finder. Operating autonomously along a predetermined flight plan, the drone captures high-frequency range data of warehouse inventory. The paper comprehensively outlines the UAV control procedures, warehouse scanning using LiDAR, and the inventory detection and quantification of pallets algorithmic process. The proposed method processes LiDAR data in a post-process way, estimating the number of pallets and, consequently, producing a map of each stack within the warehouse denoting the quantities of pallets. The research results showcase the successful implementation of the proposed approach in a model warehouse, achieving an impressive 100% evaluation accuracy. Future research endeavors aim to extend this methodology to warehouses with dynamic product placements, emphasizing real-time monitoring for comprehensive inventory detection. This innovative approach stands out as a cost-effective and efficient solution for industries seeking accurate and timely inventory information.