Research On Indoor Positioning Technology Based On Sensor Fusion

With the development of robotics and artificial intelligence technology,the indoor positioning technology of autonomous mobile robots has become a research hotspot and research direction in the field of robotics.However,there is no mature and perfect theoretical system to solve the indoor positioning problem like the GPS global positioning system solves the outdoor positioning problem.Therefore,a good indoor setting plan has become an urgent need to solve indoor positioning problems.With the maturity of artificial intelligence technology,the application of multi-sensor information fusion to the indoor positioning of autonomous mobile robots has become the mainstream method of indoor positioning.This thesis will focus on the autonomous mobile robot based on mecanum wheel,and improve the indoor positioning accuracy of autonomous mobile robots.The indoor positioning algorithm,software and hardware platform and sensor fusion algorithm of autonomous mobile robots are studied..The main work of this paper is as follows:1.Kinematic modeling of autonomous mobile robots.This paper will model the kinematics of autonomous mobile robots based on the mecanum wheel.The kinematics modeling is used to realize the calculation of the indoor positioning coordinate information of the autonomous mobile robot and the motion control of the autonomous mobile robot.2.Research based on single sensor indoor positioning method.In this paper,the single-sensor indoor positioning technology and algorithm of autonomous mobile robot will be studied.By summarizing and analyzing the problems of single-sensor indoor positioning,the necessity of multi-sensor data fusion algorithm research is introduced.3.Research on multi-sensor data fusion algorithm.This paper will study the Kalman filter algorithm and BP neural network algorithm respectively.Based on the research,an optimization Kalman filter fusion algorithm based on BP neural network is designed.The effectiveness and generalization ability of the algorithm are verified.It is concluded that the optimization Kalman filtering algorithm based on BP neural network can obtain more accurate positioning information and improve the accuracy of indoor positioning.4.Research on indoor positioning experimental platform based on multi-sensor fusion.The experimental platform of autonomous mobile robot is built.It is verified that the optimization Kalman filtering algorithm based on BP neural network can better deal with the indoor positioning problem of autonomous mobile robots.