This study presents an innovative automated methodology for identification of electrical substations' key elements from 3D LiDAR point clouds acquired by terrestrial laser scanners. The developed methodology is composed of nine... more
This study presents an innovative automated methodology for identification of electrical substations' key elements from 3D LiDAR point clouds acquired by terrestrial laser scanners. The developed methodology is composed of nine algorithms that identify objects of interest with respect to their physical shape and topological relationships among them. The objects of interest in this contribution are ground, fence, cables, circuit breakers, bushings, bus pipes, insulators, and three types of poles with circular, octagonal, and square cross sectional shape. The developed methodology incorporates a computationally-efficient algorithm for detection of ground within electrical substations; two separate algorithms for identifying well-sampled and poorly-sampled fences; robust algorithms for detecting cables, circuit breakers, and bushings with respect to their unique physical shape and the topological relationships among them; and a novel method for simultaneous identification, modeling, and registration-refinement of poles with circular and regular polygonal cross sectional shapes. The proposed methods in this study work quite robustly despite the challenges introduced by non-uniform point sampling, registration error, occlusion, attached objects, gap, dense configuration of neighboring objects, and outliers. Five datasets with quite different volume and configuration were employed in this work. The first three datasets contain point clouds of two different electrical substations. The fourth and fifth datasets contain point clouds of an urban roadway and a pole-like monument with a regular dodecagonal cross section, respectively. The obtained results indicate that 367 out of 382 objects of interest (96.1%) in the first dataset; 354 out of 382 objects of interest (92.7%) in the second dataset; and 255 out of 264 objects of interest (96.6%) in the third dataset were successfully recognized. At point cloud level, it achieved greater than 99%, 96%, and 97% average recognition precision and accuracy in the first, second, and [...]
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This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630... more
This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positi...
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According to the Department of Energy of the USA, today’s electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact... more
According to the Department of Energy of the USA, today’s electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) ...
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Research Interests: Automation, Classification, Key words, Railway, Fitting, and 3 morePoint Cloud, Markov chain, and Mls
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DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole... more
DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the Faculty. ABSTRACT The purpose of this research is to classify points on rails, contact and catenary wires from 3D lidar point clouds in a railway environment. Two dataset were used for this research i.e. a terrestrial dataset and an airborne dataset. The classification of objects in a railway environment is important as railway companies tend to have a database of their infrastructures due to maintenance and design purposes. In addition, by automating this process companies reduce the cost of maintenance substantially and also by using 3D lidar data they obtain more precise data of their infrastructures. The idea is to do a knowledge based classification which takes advantage of ...
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This study proposes an enhanced algorithm that outperforms the methods developed by the author's earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable... more
This study proposes an enhanced algorithm that outperforms the methods developed by the author's earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable to railroads with any slope; (2) employing Eigen decomposition for the rail seed point selection that makes it independent of the rails' dimensions; and (3) developing a computationally efficient fully data-driven method (simultaneous identification of rail tracks and contact cables) that is able to process poorly sampled datasets with complicated configurations. The upgraded algorithm is applied to two datasets with quite different point sampling and complexity. First dataset is scanned by a terrestrial system and contains three million points covering 630 m of an inter-city railroad corridor. It presents a simple configuration with nonintersecting straight rail tracks and cables. Second dataset includes 80 m of a complex urban railroad environment comprising curved and merging rail tracks and intersecting cables. It is scanned from an airborne platform and contains 165,000 points. The results indicate that all objects of interest are identified and the average recognition precision and accuracy of both datasets at the point cloud level are greater than 95%.
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This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630... more
This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor.
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This study proposes fully-automated methods for as-built model generation of subway tunnels employing mobile Light Detection and Ranging (LiDAR) data. The employed dataset is acquired by a Velodyne HDL 32E and covers 155 m of a subway... more
This study proposes fully-automated methods for as-built model generation of subway tunnels employing mobile Light Detection and Ranging (LiDAR) data. The employed dataset is acquired by a Velodyne HDL 32E and covers 155 m of a subway tunnel containing six million points. First, the tunnel's main axis and cross sections are extracted. Next, a preliminary model is created by fitting an ellipse to each extracted cross section. The model is refined by employing residual analysis and Baarda's data snooping method to eliminate outliers. The final model is then generated by applying least squares adjustment to outlier-free data. The obtained results indicate that the tunnel's main axis and 1551 cross sections at 0.1 m intervals are successfully extracted. Cross sections have an average semi-major axis of 7.8508 m with a standard deviation of 0.2 mm and semi-minor axis of 7.7509 m with a standard deviation of 0.1 mm. The average normal distance of points from the constructed model (average absolute error) is also 0.012 m. The developed algorithm is applicable to tunnels with any horizontal orientation and degree of curvature since it makes no assumptions, nor does it use any a priori knowledge regarding the tunnel's curvature and horizontal orientation.
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According to the Department of Energy of the USA, today's electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact... more
According to the Department of Energy of the USA, today's electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus 3D , were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed method achieves better results with higher computational speed than PCA in the segmentation of planar surfaces.
Research Interests: LiDAR and Segmentation
A considerable percentage of power outages are caused by animals that come into contact with conductive elements of electrical substations. These can be prevented by insulating conductive electrical objects, for which a 3D as-built plan... more
A considerable percentage of power outages are caused by animals that come into contact with conductive elements of electrical substations. These can be prevented by insulating conductive electrical objects, for which a 3D as-built plan of the substation is crucial. This research aims to create such a 3D as-built plan using terrestrial LiDAR data while in this paper the aim is to extract insulators, which are key objects in electrical substations. This paper proposes a segmentation method based on a new approach of finding the principle direction of points' distribution. This is done by forming and analysing the distribution matrix whose elements are the range of points in 9 different directions in 3D space. Comparison of the computational performance of our method with PCA (principal component analysis) shows that our approach is 25% faster since it utilizes zero-order moments while PCA computes the first-and second-order moments, which is more time-consuming. A knowledge-based approach has been developed to automatically recognize points on insulators. The method utilizes known insulator properties such as diameter and the number and the spacing of their rings. The results achieved indicate that 24 out of 27 insulators could be recognized while the 3 unrecognized ones were highly occluded. Check point analysis was performed by manually cropping all points on insulators. The results of check point analysis show that the accuracy, precision and recall of insulator recognition are 98%, 86% and 81%, respectively. It is concluded that automatic object extraction from electrical substations using only LiDAR data is not only possible but also promising. Moreover, our developed approach to determine the directional distribution of points is computationally more efficient for segmentation of objects in electrical substations compared to PCA. Finally our knowledge-based method is promising to recognize points on electrical objects as it was successfully applied for insulators.