Yang Kai, Deng Xiaopeng, Bai Zijian, Wan Yingying, Xie Liming, Zeng Ni
School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel). 2024 Dec 20;24(24):8146. doi: 10.3390/s24248146.
Point cloud registration is pivotal across various applications, yet traditional methods rely on unordered point clouds, leading to significant challenges in terms of computational complexity and feature richness. These methods often use k-nearest neighbors (KNN) or neighborhood ball queries to access local neighborhood information, which is not only computationally intensive but also confines the analysis within the object's boundary, making it difficult to determine if points are precisely on the boundary using local features alone. This indicates a lack of sufficient local feature richness. In this paper, we propose a novel registration strategy utilizing ordered point clouds, which are now obtainable through advanced depth cameras, 3D sensors, and structured light-based 3D reconstruction. Our approach eliminates the need for computationally expensive KNN queries by leveraging the inherent ordering of points, significantly reducing processing time; extracts local features by utilizing 2D coordinates, providing richer features compared to traditional methods, which are constrained by object boundaries; compares feature similarity between two point clouds without keypoint extraction, enhancing efficiency and accuracy; and integrates image feature-matching techniques, leveraging the coordinate correspondence between 2D images and 3D-ordered point clouds. Experiments on both synthetic and real-world datasets, including indoor and industrial environments, demonstrate that our algorithm achieves an optimal balance between registration accuracy and efficiency, with registration times consistently under one second.
点云配准在各种应用中都至关重要，但传统方法依赖无序点云，在计算复杂度和特征丰富度方面带来了重大挑战。这些方法通常使用k近邻（KNN）或邻域球查询来获取局部邻域信息，这不仅计算量大，而且将分析局限于物体边界内，仅靠局部特征很难确定点是否恰好在边界上。这表明缺乏足够的局部特征丰富度。在本文中，我们提出了一种利用有序点云的新颖配准策略，如今通过先进的深度相机、3D传感器和基于结构光的3D重建可以获得有序点云。我们的方法通过利用点的固有顺序消除了对计算成本高昂的KNN查询的需求，显著减少了处理时间；通过利用2D坐标提取局部特征，与受物体边界限制的传统方法相比提供了更丰富的特征；在不进行关键点提取的情况下比较两个点云之间的特征相似性，提高了效率和准确性；并集成了图像特征匹配技术，利用2D图像和3D有序点云之间的坐标对应关系。在包括室内和工业环境在内的合成数据集和真实世界数据集上进行的实验表明，我们的算法在配准精度和效率之间实现了最佳平衡，配准时间始终在一秒以内。
