From real to virtual sensors, an artificial intelligence approach for the industrial phase of end-of-line quality control of GDI pumps

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. 

Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

The detection of sidewall protrusions in highway tunnel is significant for the intelligent cleaning of tunnel walls. An automatic detection method for sidewall protrusions based on LiDAR in the scene of long curved tunnel walls is proposed in this paper. First, the ground point clouds are removed by using cloth simulation filter (CSF) algorithm. Then, the obstacles point clouds are clustered by combining the region growing (RG) algorithm and the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Finally, in order to separate wall protrusions and other obstacles, tunnel boundary is extracted by horizontally projecting the remaining point clouds after removing the ground point clouds. The experiments show that the proposed method in this paper can effectively detect the protrusions on the sidewall of the tunnel. 

Detection of Side Wall Protrusions in Highway Tunnel Based on LiDAR

The detection of sidewall protrusions in highway tunnel is significant for the intelligent cleaning of tunnel walls. An automatic detection method for sidewall protrusions based on LiDAR in the scene of long curved tunnel walls is proposed in this paper. First, the ground point clouds are removed by using cloth simulation filter (CSF) algorithm. Then, the obstacles point clouds are clustered by combining the region growing (RG) algorithm and the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Finally, in order to separate wall protrusions and other obstacles, tunnel boundary is extracted by horizontally projecting the remaining point clouds after removing the ground point clouds. The experiments show that the proposed method in this paper can effectively detect the protrusions on the sidewall of the tunnel.

Accurately detecting the tooth profile parameters of the synchronous belt is crucial for the transmission’s load distribution and service life. However, the existing detection methods have low efficiency, are greatly affected by the manual experience, and cannot realize automatic detection. A measurement method based on point cloud data is proposed to solve this issue. The surface space points of the synchronous belt are acquired by a line-structured light sensor, and the raw point clouds are preprocessed to remove outliers and reduce the number of points. Then, the point clouds are divided into plane and arc regions, and different methods are used for fitting. Finally, the parameters of each tooth are calculated. The experimental results show that the method has high measurement accuracy and reliable stability and can replace the original detection method to realize automatic detection.

https://www.mdpi.com/1424-8220/22/17/6372/pdf?version=1661343987

Qinghui Kong

Papers

A New Point Cloud Simplification Method with Feature and Integrity Preservation by Partition Strategy