Object Recognition And Pose Estimation Based On Stereo Vision

With the development of artificial intelligence and computer vision technology,industrial production is becoming more and more automatic.Non-contact automatic measurement technology is an important content in industrial production.Computer vision technology can solve the key problems in automatic measurement: object recognition and pose estimation.In recent years,methods based on deep learning have been continuously developed,gradually replacing the method based on manually feature extraction.Point cloud is one of the important representations of 3D data.Compared with 2D images,point cloud contains more information such as depth,and it is representationally simple to be used as input for deep learning models.This thesis is based on the stereo vision technology to obtain the target 3D point cloud,and researches on the object recognition and pose estimation technology based on the 3D point cloud.The main work is as follows:Firstly,depth cameras are compared and binocular camera's imaging principle is introduced.Then the binocular stereo vision platform is built and calibrated,which collects the point cloud data of the target scene.Because the collected point cloud data has noise and interference,it is necessary to preprocess the point cloud data.In this thesis,conditional filtering,voxel down-sampling and statistical filtering are used to filter the original point cloud to obtain preprocessed point cloud data.Secondly,based on Point Net's point cloud classification network,this thesis proposes the MV-Point Net network model.The network uses multi-view point cloud data as network input,and uses View-Pooling to aggregate multi-view features.An evaluation experiment was conducted on the model using different View-Pooling methods and numbers of views,and compared with Point Net.The method gains an accuracy rate of 86.5% in the dataset made based on Model Net.The recognition of the real object point cloud experiment was conducted based on the point clouds gained from stereo camera to test the models.Finally,to estimate the pose of the identified object,it is necessary to register the target point cloud with the template point cloud to obtain the transformation matrix.The combination of deep learning coarse registration and ICP fine registration was proposed,whose accuracy is improved compared with the independent algorithms and it is two times faster than the traditional ICP algorithm.