Research On Indoor Mobile Robot Positioning Based On Multi-sensor Fusion

Ultra-wide Band(UWB)technology has attracted much attention in the field of indoor positioning due to its advantages of high-precision positioning.However,in complex indoor scenes,due to the occlusion of obstacles,the UWB measurement data may appear outlier,so it is difficult to provide stable and high-precision positioning information.Inertial measurement unit(IMU),as a kind of motion sensor,is widely used in robot navigation and aircraft control.At present,there are many IMUs based on low-cost microelectromechanical systems in the market.However,factors such as bias error,scaling factor error,cross axis coupling error,and random noise in low-cost IMUs can affect their accuracy.In practice,a single UWB positioning technology often cannot provide the required positioning accuracy and stability.Therefore,the UWB/IMU combined indoor positioning method based on Extended Kalman Filter(EKF)and Unscented Kalman Filter(UKF)is studied to solve the problem of reduced positioning accuracy of UWB positioning technology in non-line-of-sight environment and the problem of accumulation of positioning error in IMU positioning technology with time.This method improves positioning accuracy by integrating UWB and IMU data,while being able to track position,speed,and direction information,enabling a higher level of indoor positioning.This article implements the above algorithms by designing an indoor positioning system.The hardware design part includes the mobile robot design based on the STM32H750 main control chip and the UWB positioning(DWM1000)module design.The software design section includes robot program framework design,firmware design of UWB module,upper computer software design,and IMU calibration and calibration.Among them,the firmware design of the UWB module mainly involves preprocessing and filtering the UWB data,as well as analyzing the communication protocol.In addition,the upper computer software designed in this article can display the real-time position of tags through images,interact with people,and improve the user experience.Finally,the test results on the experimental site indicate that the positioning system based on extended Kalman filter(EKF)and unscented Kalman filter(UKF)fusion algorithms can significantly improve the accuracy of the positioning system compared to single UWB and IMU positioning,and the UKF fusion algorithm has stronger robustness than the EKF fusion algorithm.UKF based positioning algorithms can achieve higher positioning accuracy.