This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet + + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness.

本研究提出了一种基于点云的姿态估计的创新深度学习算法，旨在解决受环境影响的物体姿态估计的挑战。之前关于使用深度学习进行姿态估计的研究主要是使用 RGB-D 数据进行的。本文介绍了一种利用点云数据进行基于深度学习的姿态计算的算法。该算法建立在之前工作的基础上，通过集成PointNet + + 技术和经典的点对特征算法，实现了不同场景尺度下物体的准确姿态估计。此外，还引入了一种适用于点云的自适应参数密度聚类方法，可以在不同的点云密度环境中有效地分割聚类。这解决了不同点云环境下密度聚类参数确定的复杂问题，增强了聚类的鲁棒性。此外，将 LineMod 数据集转换为点云数据集，并在转换后的数据集上进行实验，以通过我们的算法获得有希望的结果。最后，强光和弱光条件下的实验证明了该算法的鲁棒性。