Indoor Wireless Localization Using Consumer-Grade 60 GHz Equipment with Machine Learning for Intelligent Material Handling

Abstract

Wireless indoor localization is critical for autonomous agents in modern and future smart warehouses. Millimeter-wave (mmWave) frequencies have been investigated for high-precision localization in recent years for indoor as well as outdoor positioning. We propose machine learning (ML) techniques over a radio map to estimate the location of an autonomous material handling agent used in warehouses. Based on our experimental results we demonstrate that a Multilayer Perceptron (MLP) based positioning achieves centimeter level accuracy with Root Mean Square Error (RMSE) of 0.84m. The proposed localization technique achieves up to 80% lower positioning error compared to state-of-the-art mmWave wireless localization techniques.