基于自适应局部邻域条件下的点云匹配

Point cloud matching algorithm based on adaptive local neighborhood conditions

为了应对传统迭代最近点(ICP)算法在处理复杂点云空间特征时,面临噪声干扰和数据缺失等问题导致收敛速度缓慢、配准精度不高以及鲁棒性较差等问题,本文提出了一种基于自适应局部邻域条件下的点云匹配算法。首先,采用体素网格滤波对数据进行预处理,根据不同半径邻域内邻近点的分布情况,定义邻域表面的弯曲程度,在此基础上,充分考虑到法向量分布和邻域曲率特征,从而得到更精确的特征点提取;其次,通过运用最小二乘曲面拟合方法,进一步提取出邻域曲率变化最为显著的特征点,采用快速点特征直方图(FPFH)对特征点进行描述,并通过设定距离阈值的采样一致性算法来匹配相似的特征点对,计算出关键的坐标转换参数,完成初始配准。最后,利用线性最小二乘优化点到面的ICP算法,以实现更精确的配准结果。通过一系列实验对比发现相较于现有的几种配准算法(ICP,SAC-IA+ICP,K4PCS+ICP),在存在噪声干扰和数据缺失的情况下,所提方法的配准准确度平均提高45%,配准速度平均提高38%,充分验证了该方法在应对大数据量、低重叠率点云配准方面具备出色的稳健性能。

To address the issues faced by traditional Iterative Closest Point(ICP)algorithms in handling complex point cloud spatial features,such as noise interference and data loss leading to slow convergence,low registration accuracy,and pool robustness,this paper proposed a point cloud matching algorithm based on adaptive local neighborhood conditions.Initially,voxel grid filtering was used for data preprocessing,and the curvature of neighborhood surfaces was defined based on the distribution of nearby points within different radii. Considering the distribution of normal vectors and neighborhood curvature features,more accurate feature points were extracted. Subsequently, the most significantly changing curvature featurepoints in the neighborhood were further extracted using the least squares surface fitting method.These points were described using the Fast Point Feature Histograms (FPFH), and similar feature pointpairs were matched using a sample consensus algorithm with a set distance threshold. This calculated thekey coordinate transformation parameters to complete the initial registration. Finally, a linear least squaresoptimization point-to-plane ICP algorithm was used to achieve more accurate registration results. Comparativeexperiments demonstrate that, under conditions of noise interference and data loss, the proposedmethod improves registration accuracy by an average of 45% and increases registration speed by 38%,compared to existing algorithms (ICP, SAC-IA+ICPK4PCS+lCP), thus confirming its excellent robustnessin handling large-volume, low-overlap point cloud registrations.