6D DBSCAN-based segmentation of building point clouds for planar object classification

Czerniawski, Thomas; Sankaran, Bharathwaj; Nahangi, Mohammad; Haas, Carl T.; Leite, Fernanda

doi:10.1016/j.autcon.2017.12.029

Cited by 100 publications

(31 citation statements)

References 13 publications

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“…The stacked supervised learning method [30] was then applied to classify the planar segments into objects such as walls, floors, and openings. Reference [31] first employed a combination of random sample consensus (RANSAC; [32]) and density-based attribute clustering (DBSCAN), as proposed by [33], to group together planar points of a completed indoor building. Eighteen geometric features, including the distance between a plane's centroid and scan boundaries, were then calculated for each planar patch and fed to a k-means clustering and supervised learning framework to predict the object class (e.g., wall) that best matched the features.…”

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confidence: 99%

“…However, point clouds acquired from construction sites contain outliers due to dust, occlusions, and moving objects, which requires additional robust outlier removal procedures [22]. Other group of studies that focus on semantic labeling of point clouds acquired from construction sites mainly require either an up-to-date 4D BIM [15][16][17][18][19][20][21][22]25] or a library of historical preclassified objects [23,24,[26][27][28]31], which may be neither available nor practical. In addition, to provide a generalizable solution, a point cloud processing framework is required whose effectiveness is independent of subjectively predefined thresholds [22].…”

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confidence: 99%

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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“…The DBSCAN is a density-based clustering algorithm proposed by Sander, J. et al [26] in 1998, which is widely used in the fields of physics [27], computer science [28,29], medicine [30], architecture [31], agriculture [32] and so on. Compared to other clustering methods such as K-means and Gaussian mixtures, the advantages of the DBSCAN method lie in the following aspects: (1) It has better identification capability for abnormal points.…”

Section: Diagnosis Methodsmentioning

confidence: 99%

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Zhang

Liu

et al. 2019

Energies

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Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

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“…DBSCAN is a well-known clustering algorithm that maps the density of samples [38][39][40][41][42]. With the ability of unsupervised-learning, DBSCAN can directly extract clustering information of data which can be utilized in identifying some widely used MFs such as PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM without training data.…”

Section: Introductionmentioning

confidence: 99%

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

et al. 2020

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In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).

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6D DBSCAN-based segmentation of building point clouds for planar object classification

Cited by 100 publications

References 13 publications

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

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