A 3DoF Pose Estimation Method for MultiTrolley from a Single RGB Image 3DoF pose estimation for multitrolley is pivotal in Automatic Collection Trolley Robot System. Because of the challenge of occlusion and lowresolution problem, the existing methods do not solve these problems well. In this paper we propose a 3DoF pose estimation method for multitrolley based on the keypoints from a single RGB image. The proposed method is divided into three steps: Firstly, YOLOv3 is used to detect the trolley in a RGB image and crop the trolley individually. Secondly a new network including two parts is proposed for detecting the 2D keypoints of the trolley. One part called DetectNet is used for detecting easy keypoints, while the other part called HardNet is for detecting hard keypoints under occlusion followed by postprocessing for finetuning. Finally, 3DoF pose estimation of a trolley can be obtained by the corresponding relationship between the 2D keypoints and the real 3D keypoints in trolley 3D model. Experiments have been carried out to validate the proposed model. The results show that our approach is accuracy and robust for 3DoF pose estimation of trolley.

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%.