Visual Location And Path Planning Based On RGB-D SLAM

With the development of global technology,mobile robotics is not limited to academic research and slowly penetrates into all fields,such as industry,medical treatment,military,adventure,household products and so on.Intelligent robot can sense and recognize the surrounding environment.Through the surrounding environment,the robot can carry out self-learning,dynamic decision and planning.However,robot navigation is the basic problem of the mobile robots,and the important research direction of the mobile robots.If the robots want to achieve self-navigation,self-positioning,mapping and path planning are the main problems that should be solved firstly.In my article,the self-positioning and mapping are solved through the RGB-D SLAM frame,it includes image processing front and optimized backend.at the front part,For the feature detection and extraction,the Kinect sensor often has appearance noise and depth noise when it gets image data,the traditional visual-only features are often affected,a novel approach is proposed in my article that first extracts planes from the point cloud,acquired by the RGB-D camera,the appearance noise can be filtered,then tries to enhance the texture on the extracted planes,the depth of features can be corrected through the parameters of plane models,it can reduce the effect of depth noise,then point features are then detected.For the feature matching,the traditional algorithm,BF and FLANN has some wrong matches,so the KNN based RANSAC is used to remove the wrong matching,the KNN can detect the duplex matching,using homography transformation to optimize the matching results,and using RANSAC to filter the rest part.For the motion estimation,using ICP based RANSAC to estimate the parameter of robot's motion,because the traditional algorithm easily make the ICP fall into the local minimum.The optimized backend includes building the pose map through the front data,eliminating wrong loop closure and pose graph optimization using g2 o.For the robot path planning,using the 16 direction instead of 8 direction based A*algorithm,could reduce the length of actual running route and the sum of rotation angle,using the improved method could reduce the energy of robot,at last,using the improved A* algorithm to perform global planning and using the dynamic A* algorithm to perform local planning.