Research Of LiDAR Object Detection Based On Multi-view Data Representation

Object detection is an important task in computer vision.Lidar sensors can quickly and accurately measure the three-dimensional coordinates of targets in the scene actively,and are widely used in autonomous driving,robotics,augmented/virtual reality and other scenarios.Based on the feature expression,the current mainstream point cloud target detection methods can be divided into four categories:one kind is methods based on point features,the other kind is methods based on the bird's eye view,the third is methods based on the front view,and their hybrid methods.Point-level operators can effectively represent features,but they are computationally expensive and difficult to apply to large-scale point cloud datasets.Bird's-eye-view-based methods are more accurate,but incur quantization errors when dividing the space into voxels.The front-view methods is computationally efficient,but depth estimation of targets is inaccurate.This paper designs a target detection method under a new view,and further explores how to use multi-view fusion to take into account the effect and efficiency of the model.A cross-sensor point cloud simulator is proposed to narrow the model performance gap.First,this paper proposes a new 3D object detection method based on cylindrical view.The method uses a cylinder feature extraction network with outstanding performance as an encoder,and applies context-structure-aware asymmetric convolution and dilated sparse convolution to the backbone network and region extraction network to effectively extract appearance information and position information,and solve objects under cylindrical view.Serious deformation and scale inconsistency.In order to adapt the threedimensional target frame to the cylindrical coordinate system,the method further designs a cylindrical center detection head with distance perception,which can effectively improve the positioning.Experiments show that this method can greatly improve the accuracy of small targets and long-distance targets.Secondly,this paper proposes a real-time multi-view fusion 3D object detection method,which is simple,efficient and accurate.This method combines the advantages of front view and bird's-eye view,performs segmentation on the front view,and casts the segmented foreground points and features into a 3D point cloud for detection.It is a fully sparse network,so it can efficiently aggregate multiple frames of data and learn temporal features.Compared with the same precision network,the time-consuming of this method is reduced by 5 times.Finally,to solve the problem that detection models on source domain performs bad on target domain where mechanical lidar sensors are of different number of beams,this paper proposes a mode-aware lidar simulator to simplify the calculation of sensor ray tracing and accelerate data generation.This simulator is used to generate a cross-sensor lidar point cloud object detection dataset which contains large-scale annotated lidar point clouds captured from a mixed reality lidar simulator for datasets of 6 different sensors but with the same scenes and consistent corresponding annotations.