Research On 3D Scene Reconstruction Of Autonomous Scanning Ultrasound Robot Based On RGB-D SLAM

Autonomous scanning ultrasound robots can make full use of existing medical resources,improve medical diagnosis efficiency,and ensure the safety of medical staff.Three-dimensional reconstruction is one of the three major steps of the autonomous scanning ultrasound robot,and is also the premise and foundation of the subsequent ultrasound probe trajectory planning,the importance of which is self-evident.In this paper,we focus on the 3D reconstruction technology of the autonomous scanning ultrasound robot,and use the Simultaneous Location and Mapping(SLAM)system based on RGB-D camera as the basic solution,focusing on the accurate data acquisition,the efficient extraction of image features and the global optimization of camera position.The specific work of this paper is as follows:1.A registration method of RGB camera and IR camera based on maximum likelihood estimation and stereo vision is designed.In order to improve the quality of the data acquired by the 3D reconstruction system,this paper firstly gives the mathematical model of the camera and the aberration model,and uses the Zhang Zhengyou calibration method to obtain the internal reference matrix and aberration coefficients of the camera.Then,for the problem that color images and depth images cannot be aligned in RGB-D cameras,a registration method of RGB cameras and IR cameras based on maximum likelihood estimation and stereo vision is designed based on the imaging principle of RGB-D cameras,and the algorithm is experimentally verified to improve the alignment accuracy of color images and depth images effectively.2.An improved SLAM front-end visual odometry implementation scheme is proposed to solve the problem that the existing point feature-based visual odometry has reduced robustness in the face of low-texture and low-feature environments.First,in order to enable the 3D reconstruction system to extract effective features even in low point feature environments,this paper adds line feature extraction to the ORB(Oriented FAST and Rotated BRIEF)point feature extraction algorithm,proposes an improved LSD(Line Segment Detector)line feature rejection algorithm,and improves the The Random Sample Consensus(RANSAC)algorithm is improved to achieve the rejection of mis-matched point and line features.Finally,the experimental comparison with several mainstream point feature extraction algorithms verifies the advantages of ORB algorithm in computational efficiency;a two-frame point cloud alignment task is designed to verify the effectiveness of the improved LSD line feature extraction algorithm and the improved RANSAC feature mis-matching algorithm proposed in this paper.3.A SWPG-BA-based SLAM back-end nonlinear optimization algorithm is proposed to solve the problem of error accumulation in existing RGB-D SLAM systems when dealing with large-range and long-duration 3D reconstructed scenes.Firstly,based on the principle of uniformity of key frame distribution in time,space and feature points,a SLAM key frame screening strategy is proposed,which can effectively solve the problem of slow nonlinear optimization.Secondly,this paper improves the BA optimization algorithm and proposes the SWPG-BA(Slid Window And Pose Graph-Bundle Adjustment)nonlinear optimization algorithm,which solves the problem of reduced efficiency of the classical BA(Bundle Adjustment)optimization algorithm when dealing with long time and large data volume of nonlinear optimization.The problem is solved.In addition,in order to further reduce the cumulative error of the camera pose,the loopback detection algorithm based on the bag-of-words model is added in this paper,and the loopback correction is performed by the SWPG-BA algorithm.The combination of the SWPG-BA optimization algorithm and loopback greatly reduces the cumulative error of the SLAM system when reconstructing the scene in 3D for a long time.