Hu Jinchao, Xu Xiaobin, Cao Chenfei, Tian Zhenghong, Ma Yuanshan, Sun Xiao, Yang Jian
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China.
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China.
Sensors (Basel). 2024 Oct 12;24(20):6584. doi: 10.3390/s24206584.
In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.
在施工过程中，对既有钢筋结构进行混凝土浇筑和振捣是必要工序。本文提出一种基于三维激光雷达点云特征提取的自动振捣位置检测方法。与基于图像的方法相比，该方法对光照具有更好的抗干扰性能，且计算量更小。首先，利用激光雷达扫描获取多帧局部钢筋点云。然后，通过正态分布变换（NDT）进行点云拼接以实现初步匹配，并采用迭代最近点（ICP）算法进行精细匹配。基于图的优化（g2o）方法进一步提高了三维配准的精度。之后，将三维点云投影到二维图像中。最后，通过点云与图像处理技术识别混凝土振捣点和混凝土浇筑点的位置。实验表明，所提出的自动方法优于ICP和NDT算法，分别将均方误差（MSE）降低了11.5%和11.37%。识别混凝土振捣点和混凝土浇筑点的最大偏差分别为0.059±0.031米和0.089±0.0493米，满足混凝土振捣检测要求。
