针对传统关键点检测算法对噪声敏感及依赖于物体模型的形状特征等问题,提出一种基于正态加权的多尺度关键点提取算法。首先,在每个尺度上建立局部邻域的协方差矩阵,计算局部坐标系落在前两个坐标轴的比率大小,根据比率大小来确定候选关键点。然后计算基于正态加权的形状索引值,以此来度量点云的局部最大相异性度量值。最后,在不同尺度下,将具有局部最大相异性度量值的极大值点作为最终关键点。实验结果表明,相比较其他的传统算法,所提算法能有效地提取各种点云模型的关键点,能够同时兼顾关键点的质量、数量及运行效率,且对具有尖锐特征和大面积平滑特征的模型具有较强的适应性,算法的鲁棒性及形状索引功能得到进一步增强。

This study proposes a multi-scale key point extraction algorithm based on normal weighting to address the sensitivity to noise and dependency on object models' shape features in traditional keypoint detection algorithms. First, at each scale, the covariance matrix of the local neighborhood is established and the ratio of the local coordinate system appearing on the first two axes is calculated. Thus, candidate keypoints are determined based on the ratio. Then, to measure the local maximum dissimilarity measured value of the point cloud, the normal weighted shape index value is calculated. Finally, the maximum value point of the local maximum dissimilarity measured value at different scales is selected as the final keypoint. The experimental results show that compared with other traditional algorithms, the proposed algorithm can effectively extract keypoints of various point cloud models and simultaneously consider the quality and quantity of keypoints and operating efficiency. Moreover, the proposed algorithm has strong adaptability for models with sharp features and large area smooth features, which enhances its robustness and shape index function.