| The mobile robot indoor navigation and positioning technology is one of the key technologies of intelligent mobile robots.The integrated positioning system based on multi-sensor fusion is the focus of the current research on indoor positioning of mobile robots.In particular,the UWB(Ultra-Wideband)positioning technology can provide single point positioning information,but the positioning results are easy to be interfered by obstacles;the inertial navigation technology can provide the stable positioning results with a high level in a short period;and the visual SLAM technology can provide rich information of the surrounding environment according to the camera output,which can be used to estimate the local relative positioning results for mobile robots,the positioning results are easy to be affected by the environment.Therefore,this research studies the key technologies of UWB/inertial/visual indoor integrated navigation positioning system.In this research,the information fusion methods of UWB,IMU,and visual sensors are studied,the ill-posed problem in UWB positioning is studied,and the acquisition methods of the anchors’ location information in the UWB positioning system are studied,and a mobile robot indoor combined positioning system prototype has been built to demonstrate the methods proposed in this research.The main work of the dissertation is as follows:(1)The basic principles of inertial navigation,UWB positioning,and visual slam are summarized.The three-dimensional solution formulas of typical UWB positioning algorithms for the three-dimensional positioning problem of the mobile robots are given.A method for improving the linearization process of typical algorithms is given.And the UWB three-dimensional positioning algorithms based on ranging are implemented.(2)For UWB positioning,the numerical relationship of UWB positioning errors in all directions of positioning results is analyzed,and a factor called the maximum distance scale factor is proposed to describe positioning errors in the UWB positioning system.Then,the reason for UWB indoor positioning equations with a large condition number is given.To solve the ill-posed problem in UWB positioning,an optimization regularization method based on the regularization principal and Tikhonov regularization method is derived and proposed.The optimization regularization algorithm based on re-analysis is proposed to solve the problem that the estimation result is biased due to the introduction of regularization parameters,and the general calculation of regularization parameter value is given.In particular,the proposed optimization regularization method can not only solve the ill-posed problem in UWB indoor positioning but also solve other similar problems,such as the optimization problems that can be solved by applying the least square estimation.The simulation and prototype experiment results demonstrate the effectiveness of the method.(3)UWB,inertial,and visual integrated navigation and positioning under incomplete anchor information are studied.When the location information of the UWB anchors can not be obtained completely in advance,a method of determining the remaining location information of the anchors based on the initial UWB network composed of four UWB anchors is studied.In this dissertation,a scheme to estimate the new UWB information using the anchors’ location and ranging values between the mobile robot and anchors is proposed.In the scheme,an optimized recursive least square method(RLSM)is applied to realize the goal.Then,an integrated positioning system based on the maximum correlation entropy Kalman filter(MCKF)is constructed to fuse the positioning information of UWB and SINS,and the estimation effectiveness of MCKF combined with the optimized RLSM is verified based on the system.The data conversion problem between each different positioning subsystem is analyzed,and a nonlinear optimization method to estimate the conversion relationship between different systems based on the output of the same measurement in different systems is described.Then,a method of optimizing and fusing the information of UWB,inertial,and visual subsystem based on a factor graph is described.In this method,the estimated residuals between the integrated system and different subsystems are constructed as the optimization variables,and all the optimization variables are optimized by bundle adjustment to integrate all the positioning information and finally provide stable and robust positioning results.(4)A prototype platform of an intelligent mobile robot is built,and the integrated navigation and positioning system of UWB,inertia,and vision based on multi-sensor fusion is established.Based on the prototype platform,many experiments are designed to demonstrate the effectiveness and effect of the proposed scheme and algorithm.Among them,the feasibility and effectiveness of the acquisition method for the new UWB anchor’s position information scheme based on the UWB ranging values and mobile robot positions are verified through the experiments,and the effects of various implementation methods are compared.Finally,the positioning effect and robustness of the UWB/inertial/visual integrated navigation positioning system based on the factor diagram optimization are demonstrated. |
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