Research On SLAM Method Based On Monocular Vision And Inertial Navigation Data Fusion

The Simultaneous Localization And Mapping(SLAM)system is widely used in the field of mobile robots.Because of its small computation and simple structure,monocular vision slam system has become a research hotspot.But,its disadvantages is that it is easy to lose information when the scene texture is weak or the motion is too fast,and the monocular camera cannot obtain the absolute scale of the environment,The Inertial Measurement Unit(IMU)can estimate its own angular velocity and acceleration to obtain absolute scale information,and can provide position information in the environment of lack of camera information.At the same time,the camera information can correct the accumulated drift of IMU.Based on this,this dissertation studies a slam method of monocular vision and INS data fusion,which can improve the positioning accuracy and robustness of slam system.In this dissertation,camera pose estimation and camera and IMU calibration methods are studied.Firstly,KLT tracking algorithm is used to match the feature points extracted by ORB algorithm.The MSAC algorithm is used to eliminate the mismatches in the matching process,which overcomes the problem that the threshold selection of RANSAC algorithm is too sensitive.Secondly,in order to solve the problem of large amount of computation in the current camera pose estimation algorithm,the improved DLT algorithm is introduced to estimate the camera's pose.Compared with the current mainstream EPNP algorithm,the algorithm speed of pose estimation is effectively improved.Finally,the method of calibrating camera and IMU with kalibr tool is studied.Based on the camera pose estimation,the joint initialization and optimization of vision and IMU are studied.Firstly,the camera information is initialized by the vision-only SFM algorithm to obtain the initial value needed for the operation of the slam system.Secondly,the camera coordinate system and IMU coordinate system are transformed to the world coordinate system through the joint initialization of the camera and INS data by using the rotation matrix.Finally,the optimization equation is constructed by the way of tight coupling between camera and INS data,which reduces the local drift caused by different sampling frequency.Aiming at the global drift of the system,a scheme of loop detection and global pose optimization are used to ensure the overall consistency of the system.The slam method based on monocular vision and INS data fusion is validated by building an experimental platform.Firstly,the calibration experiment of camera and IMU are conducted,and the internal and external parameter matrix of the camera is determined,and the re-projection error is less than one pixel.Then,using the data set of Eu Roc and the actual environment to locate and map,the high precision and the good robustness of the slam method in this dissertation are verified.