Research On Simultaneous Localization And Mapping Method Based On Polarized Skylight Sensor

In recent years,mobile robots have been used widely in aerospace,vehicle transportation,logistics storage and other fields.Mobile robots can not only replace manpower to accomplish heavy repetitive tasks,but also work in harsh environments,which improve production efficiency and reduce production costs.Simultaneous Localization and Mapping(SLAM),as the key technology for mobile robots to realize autonomous movement,is attracted extensive attention.SLAM means that robots start from the unknown sites in an unknown environment and repeatedly observe the map features in the movement process to confirm their pose.Then,the map is constructed incrementally according to the pose.In recent years,even if the mobile robot SLAM has been greatly studied and has made some achievements,how to realize the high-precision positioning and mapping is still the difficulty and hot point of research.In this paper,the experimental platform carrying with the speedometer,lidar and the polarized light sensor is built.By designing the filter,the mobile robot SLAM method is implemented and the accuracy and autonomy of positioning and mapping are improved.The main contents are arranged as follows:(1)Sensors commonly used in mobile robot navigation systems such as odometer,lidar,gyroscope,accelerometer and compass are introduced and compared.Based on the kinematics law of the mobile robot,the kinematics model of the mobile robot is established.(2)In order to improve the accuracy and autonomy of SLAM,based on bionic mechanism,atmospheric scattering model is introduced.Rayleigh scattering and Mie scattering model are discussed.Based on scattering model,the principle of polarized light sensor is studied and the measurement model of the polarized light sensor is established.(3)Aiming at the problem that the data of lidar are difficult to correlate,the method of extracting feature points named 1D-Scale-Invariant Feature Transform is introduced.After comparing the line extraction methods,Split-and-Merge method is selected as the line extraction method.Also,the distance histogram is used for feature description and matching.The measurement model of lidar is constructed on the basis of data association.(4)Based on the kinematics model of mobile robot and the measurement model of polarized light sensor and lidar,the system state and measurement equation of multi-sensor are established.EKF-SLAM method and Federal EKF-SLAM method are designed to improve positioning and mapping accuracy.Finally,the validity of the proposed method is proved by setting up an experimental platform and conducting outdoor experiments.Aiming at the problem that mobile robots need high precision and strong autonomous SLAM,the kinematics model of mobile robots and the measurement model of sensors are established.EKF and federated EKF are designed to realize SLAM of the mobile robot.The feasibility and validity of the method are verified by building an experimental platform,which provides a reference for the application of the mobile robot SLAM.