Research on 3D Object Pose Estimation Method in Intelligent Logistics In this paper, we propose a 3D object pose estimation method based on the frustum point cloud for industrial picking robot to grab the express. The core of our idea is to segment the point cloud of the object which should to be pick up first, then extract the best grab plane and grab point on the object point cloud. First of all, a mature 2D detection framework Faster RCNN is used to detect the object in RGB images. Then the 2D bounding box and category of the object closest to the camera will be output. On the basis of this, the frustum point cloud data of the detected object is generated by the combination of the depth map and the projection relationship of the RGBD camera. Then we used PointNet network to extract the feature of each point in the generated frustum point cloud. Finally, the instance segmentation of the point cloud and the subsequent extraction of the best grab plane and grab point are performed. We show results on both real dataset and synthetic dataset which we collected by ourselves, and our 3D instance segmentation network accuracy on real dataset reached 88%.

PPRNet:Pointwise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Binpicking Scenarios Accurate object 6D pose estimation is a core task for robot binpicking applications, especially when objects are randomly stacked with heavy occlusion. To address this problem, this paper proposes a simple but novel Pointwise Pose Regression Network (PPRNet). For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to. We argue that the regressed poses of points from the same object instance should be located closely in pose space. Thus, these points can be clustered into different instances and their corresponding objectsxe2x80x99 6D poses can be estimated simultaneously. In our experiments, PPRNet outperforms the stateoftheart approach by 15%  41% in average precision when evaluated on the benchmark Sileane dataset. In addition, it also works well in real world robot binpicking tasks.