Research On Binocular Vision SLAM With Imu Information Fusion

With the development of artificial intelligence and robotics technology,robots play a more and more important rule in people's lives.For mobile robots,localization has always been a core capability.The purpose of This article is to use the IMU(Inertial Measurement Unit)to improve the performance of the visual SLAM(simultaneous localization and mapping).First of all,this article introduces the camera's pinhole model,binocular vision model,binocular distance measurement principle and other basic knowledge of binocular vision.Then FAST feature points,BRIFF descriptors,and ORB feature points is introduced.Afterwards,the method of loop detection is introduced.Secondly,this article establishes a noise model and a kinematics model for the IMU,and pre-integrates the measured quantities of the IMU.And the noise propagation equation is derived and the influence of random walk is analyzed.Then,in this paper,IMU information and visual information are combined using nonlinear optimization methods A method of saving the map and how to use the map for more precise and fast localizatio is also given later.Finally,experiments are designed and conducted based on the proposed algorithm.Among them,the calibration experiment calibrated the parameters of camera and the external parameters of the camera and the IMU.Experiments using the Euroc dataset compare the error between the localization results of this algorithm and the ground truth.Finally,the map of some real scene around us is build using the mynt-eye binocular camera.