Three-dimensional inspection method for striped steel stockpiles

As an important material handling equipment in heavy industries such as steel and metallurgy, gripper machine is widely used for gripping and transferring heavy objects such as steel. However, the traditional gripper mainly relies on manual control or simple automation procedures, in order to meet the demand of unmanned or intelligent gripper and realize intelligent factory. Therefore, this paper utilizes the powerful feature learning and representation capability of deep convolutional neural network, and focuses on the topic of 3D attitude estimation of bar steel in industrial scenarios, taking stereo vision as the basis and combining with the target LiDAR point cloud information, to study the 3D attitude estimation method of bar steel based on stereo vision and LiDAR point cloud. In order to realize the 3D attitude estimation of strip steel, a multimodal feature fusion network based on stereo vision and LIDAR point cloud is proposed in this paper. The experimental results show that the multimodal interaction method proposed in this paper significantly improves the accuracy of strip steel 3D detection, and our overall accuracy is improved by 2.91% compared with similar fusion methods.