Research On Simultaneous Localization And Mapping Technology Of Service Robot

With the increasing trend of aging in the world,service robots can help human beings to meet some daily needs,even replace human beings to take care of the elderly.As the core technology of service robots,the SLAM(Simultaneous Localization and Mapping,SLAM)technology has been a research hotspot in this field.This paper firstly introduces current researches of service robots and their core SLAM technology at home and abroad.Then,for the use of SLAM about indoor service robots,use monocular cameras and inertial sensors to complete the construction of indoor maps,tracking and localization of service robots' motion trajectory.The main research contents of the paper are summarized as follows:1.Use MARG(Magnetic,Angular Rate and Gravity,MARG)sensor to complete localization and tracking of indoor service robots.In order to improve the accuracy of localization and tracking of robots motion trajectory,Triaxial magnetometers are combined on the basis of accelerometers and gyroscopes to form a MARG sensor which works as an inertial sensing unit.The Proportional Integral Controller(PI)algorithm is introduced.Attitude matrix calculated by gyroscope is used to do coordinate system transformation for measurements of accelerometer and magnetometer.The transformation error generated during transformation can be used to modify the attitude matrix and improve the overall positioning accuracy.The final experimental results show that the average positioning error is about 4.96%.2.Three-dimensional reconstruction of the indoor map is completed by building a monocular camera on the service robot,based on the technology of visual SLAM.First,use the monocular camera built on the service robot to acquire images of indoor environment when the robot is moving;Then,through the feature point extraction and matching algorithm,the same feature points of two adjacent images are obtained.The matching relationship between the feature points is used to solve the camera motion transformation relationship between two adjacent frames;Finally,the triangulation method is used to solve the spatial three-dimensional coordinates of the feature points.A sparse indoor scene point cloud map is constructed using the solved three-dimensional points.3.A VINS-Mono(Monocular Visual-Inertial System,VINS)SLAM system for visual SLAM and inertial sensor data fusion is initially studied.First,introduce the components of VINS-Mono system and the role of each module;Then,aiming at this system,the visual SLAM module uses the graph optimization algorithm for preprocessing.The correction processing is performed on the pose information estimated by the camera at the initial stage.Lastly,before and after the system modification,the test data and actual data sets are used for simulation and comparison.The simulation results show that the average positioning error of the small scene and large scene data sets is only 4.67% and 5.32%.However,for the actual collected robot motion data,the estimated trajectory under this algorithm is closer to the actual movement trajectory.The whole system has high positioning accuracy and strong robustness.