基于混合特征重合区域预测的无标识点位姿测量

Pose measurement without marked points based on prediction of overlapping area with hybrid features

本文提出了一种结合三维点云测量技术与点云配准算法的无标识点位姿测量方案。针对传统点云配准算法对初始位置和噪声敏感,目标测量点云与参考点云部分重合以及密度差异大导致的异源点云配准精度低的问题,提出了一种基于混合特征重合区域预测的端到端位姿估计算法,即通过深度学习的方法实现点云配准。首先,设计点云混合特征编码模块,通过特征交互融合源点云与参考点云的特征,获得更丰富的特征表示。其次,基于混合特征设计参考点云重合掩码解码模块,保留参考点云中与源点云重合的所有重合点。最后,利用重合部分的点云特征回归两组点云的相对位姿参数,实现了目标的位姿测量。实验结果表明,该算法对异源点云配准具有较好的性能,能在保证鲁棒性的同时,显著提升点云配准精度。

The point cloud registration technique and 3D point cloud measurement technology without artificial marks are used to demonstrate a pose measurement solution in this study.The cross-source point cloud registration accuracy is relatively low due to the large density difference,especially when the measurement point cloud partially overlaps with the reference point cloud,and the typical point cloud registration procedure is susceptible to the initial position and noise.A deep learning-based end-to-end pose estimation algorithm is proposed in order to address the aforementioned issues.First,a point cloud hybrid feature coding module is created to combine the features of the source and reference point clouds through feature interaction,resulting in a richer feature representation.Second,based on the hybrid features,an overlapping mask decoding module is utilized to predict and sample all the overlapping points in the reference point cloud.Finally,using the feature of points in the overlapping area,a pose regression module is utilized to estimate the relative pose parameters of the two groups of point clouds.Experimental results show that the proposed method can significantly increase point cloud registration accuracy and has higher robustness against noise interference.