针对点云配准过程中点云数据冗余、易出现误匹配点对和配准精度低的问题,提出了一种融合超体素及几何特征的点云配准方法.首先使用超体素与法向量信息相结合的方法提取特征点;其次,在粗配准中,通过使用快速特征点直方图(Fast Point Feature Histograms,FPFH)进行特征描述,采用双向最近邻比获取初始特征点对应关系,基于法向量夹角策略和随机采样一致性(Random Sample Consensus,RANSAC)算法进行对应关系的优化,获取良好的初始位姿;最后,在精配准中,基于初始位姿与改进的迭代最近点算法(Iterative Closest Point,ICP)算法完成点云配准.通过在斯坦福数据集中进行配准实验,验证了所提算法具有更好的鲁棒性,能高效且精准的完成点云配准.
Point cloud registration optimization based on fusion of supervoxels and geometric features
To address the issues of redundancy in point cloud data,prone to mis-matched point pairs,and low align-ment accuracy during the process of point cloud registration,a method that integrating supervoxels and geometric fea-tures is proposed in this paper.Firstly,key points are extracted using a combination of supervoxels and normal vector information.Subsequently,during the coarse registration phase,feature descriptions are generated using the Fast Point Feature Histograms(FPFH)method,and then initial correspondences are established based on the feature description using a bidirectional nearest neighbor ratio approach,and the correspondences are optimized using a normal vector an-gle strategy and the Random Sample Consensus(RASAC)algorithm to acquire a robust initial pose.Finally,in the fine registration phase,an enhanced Iterative Closest Point(ICP)algorithm is used based on the initial pose.By per-forming alignment experiments on the Stanford dataset,it is verified that the proposed algorithm has better robustness and can accomplish point cloud alignment efficiently and accurately.
supervoxeliterative closest point algorithmfeature matchingpoint cloud registration
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