RGB-D SLAM Algorithm Of Indoor Mobile Robot

In recent years,with the development of artificial intelligence technology,mobile robots have been widely used in various fields of social development.Robot navigation is a basic problem of mobile robot technology.SLAM(Simultaneous Localization and Mapping)is considered to be the key to autonomous navigation of robots.Compared to monocular and binocular cameras,RGB-D cameras can capture both color and depth images of the scene,making them ideal for sensors in mobile robot SLAM.SLAM research using RGB-D cameras as sensors is called RGB-D,which is a hot research direction in the field of robotics.Therefore,this paper mainly studies the indoor mobile robot RGB-D SLAM algorithm.Firstly,the imaging model of RGB-D camera and the calibration method of camera's intrinsic are studied.In the ROS environment,the calibration of the intrinsic of the color camera and the depth camera and the calibration of the extrinsic between the two cameras are performed respectively,so that the RGB image and the depth image acquired by the RGB-D camera are aligned to generate a three-dimensional point cloud.Secondly,several aspects of the front-end of RGB-D SLAM,namely feature extraction,feature matching,pose estimation,and pose optimization are studied.Using the ORB feature point extraction algorithm based on region separation,the extracted feature points are evenly covered in the entire image.This can make full use of image information and reduce the mismatch of feature points,thereby improving the accuracy of pose estimation.The descriptors of the feature points are used to match the feature points,and a distance threshold method is adopted to effectively eliminate the mismatched pair of points.Using the RANSAC+EPNP algorithm to solve the pose transformation can eliminate the outer points of feature point matching and improve the robustness of pose estimation.Using the solved poses,the 3D points are reprojected to the pixels on the image,and then reprojection errors are constructed.Furthermore,the camera pose is further optimized using a non-linear least squares method.The accuracy of pose estimation is evaluated using the RGB-D dataset provided by TUM.The results show that nonlinear optimization can effectively improve the accuracy of pose estimation.Then,a global optimization algorithm for posture of the back-end robot based on the bag of word model is studied.K-means clustering algorithm is used to cluster ORB feature descriptors to generate visual dictionary.In order to improve the efficiency of the query dictionary,the dictionary is stored in the form of a k-d tree,and the leaf nodes represent the words in the visual dictionary.Use the words in the dictionary to describe the image vector,and use the description vector to measure the similarity between the images.The global pose of the robot is optimized using the pose graph optimization model and the g2 o optimization tool.Further use the optimized camera pose to stitch the point clouds collected by different cameras to generate an environmental map.Using the RGB-D dataset provided by TUM,the accuracy of globally optimized pose estimation is evaluated,and a point cloud map and an octo-tree map are generated.An octo-tree map was constructed using an RGB-D camera for real lab scenes.