基于深度学习的物体点云六维位姿估计方法

物体位姿估计是机器人在散乱环境中实现三维物体拾取的关键技术,然而目前多数用于物体位姿估计的深度学习方法严重依赖场景的RGB信息,从而限制了其应用范围.提出基于深度学习的六维位姿估计方法,在物理仿真环境下生成针对工业零件的数据集,将三维点云映射到二维平面生成深度特征图和法线特征图,并使用特征融合网络对散乱场景中的工业零件...

Deep Learning-Based 6D Object Pose Estimation Method from Point Clouds

Object pose estimation is a key technology required for enabling the robots to pick 3D objects in a cluttered environment. However, most of the existing deep learning methods for pose estimation rely heavily on the RGB information of the scene, which limits their applications. To address the problem, a deep learning-based method for 6D object pose estimation is proposed. A data set for industrial parts is generated from physical simulation, and then the 3D point cloud is mapped to the 2D plane to generate a deep feature map and normal feature map. On this basis, a feature fusion network is used for 6D pose estimation of industrial parts in cluttered environments. Experimental results on the simulation data set and the real data set show that the proposed method improves the accuracy of pose estimation and reduces time consumption compared with traditional point cloud pose estimation methods. In addition, the method displays high robustness to the point clouds with different density and noises.