Research On Simultaneous Location And Mapping (SLAM) Based On RGB-D Picture

Recently,with the development of robotics,its core technology-Simultaneous Location and Mapping(SLAM)has also been widely concerned.In the field of visual SLAM,with its advantages of simultaneous access to color images and depth images,RGBD camera is widely used in the solution for indoor SLAM.However,there are still many problems to be solved urgently in the real-time and accuracy aspect of the current RGB-D SLAM algorithm.Therefore,based on the original solution of RGB-D SLAM,this thesis has made a much deeper study on the important parts of SLAM system.Firstly,in the part of SLAM frontend,the different feature extraction algorithms and different feature matching algorithms are compared and analyzed,and an improved RANSAC algorithm based on the adaptive method is proposed and applied to the pose estimation.By doing comparative experiments,it's proved that proposed RANSAC algorithm can effectively improve the accuracy,stability and efficiency of the pose estimation.Secondly,in the parts of SLAM backend and mapping,a key frame extraction method combining the method of inliers determination and relative motion-size determination is proposed.Based on the research of key frame extraction and loop-closure search,a global pose optimization graph is established and solved by g2o(General Graph Optimization).At the same time,this thesis compares and analyzes the different two forms of the map.Then,based on the above study,the experimental platform of RGB-D SLAM system is established and its experimental study in real environment is carried out to verify the feasibility of the system proposal.What's more,by doing the comparative experiments of different algorithms in different parts based on benchmark dataset,the influence of different algorithms to the result of RGB-D SLAM system is verified,which is of great significance to promote the application of RGB-D SLAM research in daily life.Finally,the thesis summarizes the whole paper and makes a prospect for the future research direction.