Citation: | Huang Qian, Wang Zeyong, Li Jinlong, et al. Automatic recognition of bolts on locomotive running gear based on laser scanner 3D measurement[J]. Opto-Electronic Engineering, 2018, 45(1): 170532. doi: 10.12086/oee.2018.170532 |
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Overview: The detection of locomotive running gear is an important part of railway safety inspection. However, the automatic detection based on the two-dimensional image cannot directly get the three-dimensional size of the object, and is easy to be affected by light, oil, shooting angle and so on. Therefore, it is of great practical significance to study the locomotive running gear inspection system based on three-dimensional measurement technology. Line-structured laser scanner is one of the most common 3D laser scanner. In the automatic detection of locomotive based on the 3D laser scanner, how to recognize and locate the bolts on the locomotive running gear under the 3D point cloud data is one of the research focuses. In this paper, the locomotive running gear 3D point cloud data are obtained by line-structured laser scanner, and the bolts on the locomotive running gear under the 3D point cloud data are recognized and located automatically. Firstly, an appropriate bolt in the data is selected as the template, and both in the template and target regions, key points are extracted by Intrinsic shape signatures (ISS) algorithm, and Fast point feature histograms (FPFHs) of the key points are calculated to describe the 3D features. Then, the target region is matched with the preselected bolt template on basis of the Euclidean distance between FPFHs, and points in the match point set are weighted by the key points of the bolt template they have matched. Then, K-means clustering is carried out on the weighted match point set using uniform seed points, and the clusters are initially screened based on the number of points. The point cloud is divided into many blocks according to the size of bolts, and the vertices of each block are selected as the cluster seeds. Finally, the Hough transform method is used to establish a strict classifier for the clusters. The key points on the bolts are treated as several fuzzy circles of a fixed radius, so the existence and location of the bolt can be judged by Hough transformation of each cluster. An experiment is carried out for validation. In the experiment, all five bolts of the same type in the target area are successfully marked. The experimental results verify the effectiveness of the proposed method. As the three-dimensional data can directly get the target depth information, the proposed method has a good application prospect, which is expected to be a useful complement to the online railway safety inspection system.
Basic principle of line-structured laser scanner
One of the FPFH feature descriptors
Principle of Hough transform. (a) Image space; (b) Parameter space
3D line-structured laser scanner
Test data
The point cloud data for the template and target. (a) Point cloud data for template; (b) Point cloud data for target
Key points extraction. (a) Key points extraction in template; (b) Key points extraction in target
Results of match and pre-recognition. (a) Match point set; (b) Pre-recognition of bolts
Hough transform process. (a) A cluster of matching points; (b) Hough matrix
Result of bolt recognition and localization. (a) Recognition of bolts in the target; (b) Grayscale image of the target area