| During the epidemic,people’s activity spaces have been limited,and the indoor activities have increased,and the demand for indoor position services is increasing,and as a result,indoor positioning technology has become a research hotspot.Ultra-Wide Band,UWB is a pulse signal having a high-time resolution that can be used to perform high-precision indoor positioning.However,individual UWB positioning techniques tend to be affected by the Scenario of the Nonline-of-Sight,NLOS,resulting in a sharp increase in positioning errors,even positioning.To this end,this paper uses a small cart as a positioning carrier,and studies the indoor combination positioning techniques based on UWB and inertial Measurement Unit(IMU),which makes up for their shortcomings.First,this paper introduces the common method of UWB ranging and positioning,analyzes the advantages and disadvantages of various positioning methods,and selects the ranging method based on round-trip time as a UWB ranging method,this method does not require time synchronization of base stations and labels and is easy to implement.After determining the UWB ranging mode,this paper implements the traditional UWB positioning algorithm based on extended Kalman filtering.Aiming at the problem based on the rapid increase in positioning error in the NLOS environment,the use of the map optimization algorithm is proposed,and the UWB positioning problem is converted into a map optimization problem,and the optimization-optimized UWB positioning algorithm framework is constructed,and verified in the experiment The UWB algorithm based on graphic optimization has higher precision in the NLOS environment.Secondly,a separate UWB positioning method is difficult to completely overcome the influence of NLOS.Aim at this problem,this paper introduces the UWB/IMU tight combination positioning method based on Kalman filtering.On this basis,a UWB/IMU tight combination method base on multi-state constraint is proposed.This method uses a multi-time UWB status to constrain the IMU status,and can utilize UWB historical data more efficiently,which can effectively improve the positioning accuracy of the UWB/IMU combination system.Through experiments,the UWB/IMU combination positioning method can effectively overcome the validity of NLOS errors and multi-state-based UWB/IMU positioning algorithms.Finally,for the traditional Karman filtering algorithm,it is necessary to establish a fixed and accurate mathematical model in advance,and the system extension is poor,in order to solve these problems,a UWB/IMU compact positioning algorithm based on factor map optimization method is proposed.Factor map optimization method is often used in a visual location method,which is very suitable for processing data fusion issues of different sensors.The method is highly flexible,and the combination of plug-and-play uses different sensors.If there is a sensor failure,it can be deleted in time factors corresponding to the failure sensor.Experiments show that the UWB/IMU combination positioning method based on the factor map optimization is better in the line of sight and nonvisualization compared to the traditional Karman filtering algorithm. |
