Multi-target Recognition In Complex Scenes Based On Point Cloud

Computer vision technology is a technology that studies how machines "see" the world.Specifically,the visual information of the target is collected to the computer through sensors such as camera,millimeter-wave radar,and lidar,and a series of processing of these data is performed to realize recognition,detection,segmentation and other functions.Recognition technology means that the machine "discovers" the target through sensors,and obtains the target's type,direction,speed and other information through a series of calculations and analysis.Recognition technology is widely used in various fields,such as manufacturing,security,robotics,transportation,military,etc.Recognition technology can be divided into image recognition,point cloud recognition,multi-sensor fusion recognition,etc.according to the data collected by sensors.In recent years,with the rapid development of deep learning technology,the fields of target recognition,detection,and segmentation have also developed rapidly.The main work of this paper is divided into three parts:First,the filter parameters are adjusted adaptively according to the resolution of the point cloud,and a key point search algorithm based on the mean value of the curvature of the pre-key point neighborhood is proposed.The algorithm does not depend on the curvature value of a single point,which enhances the robustness to noise and reduces the repetitiveness of key points on the same local area.A feature descriptor based on the normal relationship between the distance and the center of gravity between each point in its neighborhood is proposed,which improves the robustness to noise points and ensures the uniqueness of the descriptor.The registration experiment verifies the effectiveness of the feature descriptor.The proposed feature descriptor has good accuracy,computational efficiency and anti-noise ability in registration experiments.Then,the original point cloud processing method for constructing composite voxel features is introduced,which reduces the amount of calculation for subsequent steps by deleting some points and performing loop voxel filtering.The method of constructing voxels for the processed point cloud is introduced,and the processed point cloud data is divided into voxels according to the ratio of one point.The process of voxel construction is described in detail,all points in each voxel are sampled,deep feature extraction is performed,key point search is performed on all points in the voxel,and feature descriptors of the local curvature histogram are performed for key points Construct,integrate local curvature histogram features into deep voxel features,and perform pooling and dimensionality reduction for composite features.The composite feature is theoretically explained and feasibility analysis.Finally,the identification process of the complete network is introduced.After the feature map is constructed,the target is detected and identified through the modified RPN network.Introduce the structure and usage of the RPN network,sample features through multiple 3D convolution kernels,and finally obtain a position regression map and a probability prediction map.The design of the loss function is introduced.Introduced the use of KITTI data set in training and the details of training network.Through comparative experiments,the performance of the network is verified.