Research On Simultaneous Localization And Indoor Mapping Algorithm Based On Lidar

With the development of industrial automation in our country,an incresing number of factories and storage workshops have achieved intelligent production and transportation without the need of manual participation.Automated Guided Vehicle(AGV)as a new type of intelligent transportation device plays an important role in this process.Under this background,the algorithm of simultaneous localization and mapping for mobile robot based on lidar is studied.Firstly,the coordinate system of the robot is defined,the odometer based motion model and the lidar environment perception model are built,the lidar data are read according to the lidar data type.Through comparison,the feasibility of using grid map as map model is demonstrated.For the problem of pose estimation error caused by particle degeneration based on particle filter SLAM algorithm,This paper based on bias filter theory,analyzed the reasons of particle degeneration in particle filtering,and proposed the resampling algorithm to solve the problem of particle degeneration.Simulation experiments are carried out to compare the effect of particle filter localization with different resampling algorithms.According to the experimental results,the stratified resampling algorithm was selected to solve the problem of particle degeneration in the particle filtering process.On this basis,this paper introduces the concept of Rao-Blackwellized particle filter(RBPF),and the RBPF-SLAM algorithm.In this paper,the concept of mixed proposal distribution is introduced based on the framework of the conventional RBPF-SLAM algorithm,and an improved RBPF-SLAM algorithm based on stratified resampling is proposed.Simulation experiments on running data sets show the effectiveness of the improved algorithm.Using Turtlebot as the robot platform,the SLAM experiment in the real environment is carried out with the improved RBPF-SLAM algorithm.The result shows that the improved algorithm can get better effect.Finally,the embedded transplantation of the algorithm is completed,and the localization and mapping of the AGV prototype in real environment is verified.Experimental results show that the algorithm is effective in practical application.