Research on Wheeled Mobile Robot Positioning Based on EKF Multi-sensor Data Fusion

Abstract

This paper proposes mobile robots suitable for the logistics industry must accurately locate their position and their own pose problems during the SLAM [1] (simultaneous localization and mapping)process, that is, the positioning problem before the front-end frame matching. In this paper, a sensor data fusion localization method based on extended Kalman filter is proposed, which uses ultra-wideband [2] (UWB) and relative positioning odometer [3] (ODOM) sensors make full use of their advantages and make accurate self-positioning, which enables mobile robots to build highly accurate environmental maps in complex indoor environments. The sensor data fusion method based on Extended Kalman Filter (EKF) combines the measurement data of UWB with the measurement data of the odometer to improve the positioning accuracy of the mobile robot while controlling the system cost.