Research On Mobile Robot Vision SLAM System Based On Point And Line Features

Visual SLAM refers to the process in which mobile platforms build a map of the surrounding environment by obtaining information from the camera and use the map to find their own position in real time.It is the prerequisite for the navigation of mobile robots and has become a research hotspot in the field of robotics.However,most visual SLAM systems only recognize the point features in the environment and ignore the line features in the artificial structured environment.Especially in low-texture scenes,point feature-based visual SLAM is prone to initialization failure,and pose estimation fails due to the inability to extract enough point features.In this paper,a visual SLAM system is constructed by combining the point features and the line features of the building structure in the environment to improve the positioning accuracy and robustness of the system in low-texture scenes.The main research contents of this paper are as follows:(1)Research on pose estimation algorithm based on image midpoint feature and building structure line feature.ORB point in the first extract the image features and characteristics of LSD line and characteristics on the basis of the general line,select fit the line of the world the main direction of Manhattan building structure features,and then the frame of adjacent images feature matching and to eliminate false matching respectively,finally according to the point,line feature matching between adjacent frames build projection error function,to solve the interframe camera pose.(2)Back-end optimization and loopback detection algorithms based on image midpoint feature and building structure line feature are studied.Firstly,a key frame selection method is proposed to maintain the key frame scale,and the camera pose is optimized by constructing a local map.Then,the loopback is judged according to the similarity between key frames,and the loopback information is used for global optimization.Finally,a sparse visualization 3D point cloud map is constructed.On the basis of the front-end vision odometer,a complete visual SLAM system was built by adding the back-end optimization and loopback detection with the combination of point-line features.(3)The visual SLAM system and simulation experiment environment of this paper are established.TUM data set was used for experimental comparison,and the positioning accuracy of the proposed algorithm was analyzed according to the experimental results.Meanwhile,gazebo simulation platform was used to build a laboratory simulation environment with few point features to analyze the experimental effect of the proposed algorithm in the simulation environment,laying a foundation for the testing and application of the system in the real environment.