Research On SLAM Algorithm Of Robot Vision In Indoor Textureless And Unstructured Environment

Simultaneous Localization and Mapping(SLAM)technology not only plays an important role in the research field of mobile robots,but also is closely related to navigation and Localization technology.Because visual SLAM has more price advantages than laser SLAM,it also has more research value.Therefore,the environmental factors that have the greatest impact on visual SLAM have naturally become the focus of research.Among the commonly used feature extraction algorithms in visual SLAM,The Oriented FAST and Rotated BRIEF(ORB)algorithm has certain Superiority.Thus,an improved visual SLAM algorithm is proposed in this paper based on the ORB-SLAM2 algorithm,which can also reflect better localization and mapping performance in challenging scenes without texture and structure under indoor conditions.Firstly,the algorithm in ORB-SLAM2 tracking thread is improved to be used for precise positioning in textureless and unstructured environment.In order to extract enough feature points,the ORB algorithm is improved by enhancing scale invariance and adaptively setting corner determination threshold.On this basis,an improved motion smoothing model algorithm is used to eliminate mismatches,and the improved solve Pn PRansac algorithm is used to estimate the pose.After obtaining the initial pose by solve Pn Pransac algorithm,the pose is optimized by the hybrid error reprojection method to improve the positioning accuracy of the camera in the environment without texture and structure.Secondly,a method based on differential entropy is used to select keyframes to manage map points in local mapping thread.In loop detection thread,the original loop detection method based on bag of words is substituted,and the metrical nearest neighbor search loop detection method is adopted,which is combined with graph optimization to achieve global optimization.Finally,by adding dense mapping module and point cloud filtering optimization module,the establishment of octree map in textureless and unstructured environment is realized,and the indoor textureless and unstructured simulation environment is built by Gazebo physical simulation platform under Robot Operating System(ROS).The simulation of autonomous navigation and obstacle avoidance of mobile robot is carried out.In the navigation experiment,the map reuse module is added to reduce the complexity of the experiment by saving and loading the point cloud map,and there is no need to build the point cloud map repeatedly.In this paper,the performance of the improved algorithm is verified using a public data set provided by Technical University of Munich(TUM).The octree map without texture and structure is successfully constructed by the improved visual SLAM algorithm,and the improved algorithm has the advantages of small mapping error and high positioning accuracy in the environment without texture and structure,which is also proved by the navigation obstacle avoidance experiment.