This letter proposes a prior information-assisted (PIA) point cloud segmentation network that can be effectively applied to point cloud segmentation applications in human-robot interaction scenario. The joint angles of the robots are used as prior information, which is fed into the network as an additional input in the form of a vector along with the actual point cloud of the target scene. The PIA network incorporates a point cloud generation network and a simulation-assisted segmentation network. Based on the joint angles of the robots, the generation network generates a point cloud for a simulated scene without people or obstacles. Several new loss functions are proposed to train the point cloud generation network. The simulation-assisted segmentation network extracts and compares the features of the actual point cloud with those of the simulated point cloud and segments the actual point cloud. Experiments conducted on a homemade point cloud dataset involving an industrial scene demonstrate that the proposed approach can achieve significantly improved segmentation performance and network robustness.

中文翻译：

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用于人机交互场景中点云分割的先验信息辅助神经网络

这封信提出了一种先验信息辅助（PIA）点云分割网络，可以有效地应用于人机交互场景中的点云分割应用。机器人的关节角度用作先验信息，该信息与目标场景的实际点云一起以矢量形式作为附加输入馈送到网络中。 PIA网络包含点云生成网络和模拟辅助分割网络。基于机器人的关节角度，生成网络为没有人或障碍物的模拟场景生成点云。提出了几种新的损失函数来训练点云生成网络。模拟辅助分割网络提取实际点云与模拟点云的特征并进行比较，对实际点云进行分割。在涉及工业场景的自制点云数据集上进行的实验表明，所提出的方法可以显着提高分割性能和网络鲁棒性。