A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds

Yang, Juntao; Zhang, Kang; Akwensi, Perpetual Hope

doi:10.1109/lgrs.2018.2882694

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…Another urban-focused pole-like object detection method was proposed by Yang et al ( 26 ), wherein poles are automatically detected after a four-stage process. First, building facades are filtered out to reduce the risk of false positives.…”

Section: Previous Studiesmentioning

confidence: 99%

Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

Gouda

Shalkamy

El-Basyouny

2022

Transportation Research Record: Journal of the Transportation R

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The widespread use of light detection and ranging (LiDAR) data provides a promising source for the automatic detection and inventory of roadside assets. One of the essential elements in roadside furniture is light poles. There is limited research on the mapping of light poles using point-cloud data on rural highways. In this environment, the placement of light poles within roadside clear zones often poses a safety concern, as they are related to an increased risk of collisions. Only a limited number of studies have explored the relationship between light poles and safety because of the time-consuming and labor-intensive practices of collecting light pole assets data using traditional manual methods. This paper proposes an automated approach to mapping the locations of light poles. First, the scanning vehicle trajectory is extracted, smoothed, and then used to segment the point-cloud data into smaller overlapped batches of data. Several filters are applied to extract pole-like objects from the data. The segments are combined back together, and a density-based clustering algorithm is used to group the remaining points into clusters. A geometric filter is finally applied to extract light poles. The model is tested on 28 km of data on three rural highways in Alberta, Canada. The proposed algorithm is found to be accurate relative to previous studies, with average precision, recall, and F1 scores exceeding 98% for the test segments. The proposed work can assist in the automation of light pole inventory and road safety audits by transportation agencies.

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Section: Previous Studiesmentioning

confidence: 99%

Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

Gouda

Shalkamy

El-Basyouny

2022

Transportation Research Record: Journal of the Transportation R

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“…More specifically, it approximated the skeleton of light poles using a gamma function and used the distance between points and the potential skeleton as an important criterion for segmentation. Skeleton information was also used for detecting pole-like objects in [54]. In this work, the skeletons of pole-like object candidates were derived using Laplacian smoothing, and detection was achieved with a PCA-based object recognition method.…”

Section: ) Object Extractionmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

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“…However, features similar to traffic signs and light poles, such as trees, cannot be easily distinguished. (4) The local feature method: Yang et al [14] developed a frame-based layering method to detect pole-shaped objects from point clouds automatically. However, it is not easy to accurately divide the poles when they are close to each other or include two uprights.…”

Section: Introductionmentioning

confidence: 99%

Location and Extraction of Telegraph Poles from Image Matching-Based Point Clouds

Wang

et al. 2022

Remote Sensing

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The monitoring of telegraph poles as essential features supporting overhead distribution network lines is the primary subject of this work. This paper proposes a method for locating and extracting telegraph poles from an image matching-based point cloud. Firstly, the point cloud of the poles is extracted using the planar grid segmentation clustering algorithm and the connected component analysis algorithm of the region grows according to the isolated features of the poles perpendicular to the ground. Secondly, the candidate telegraph poles are located based on the suspension point of the buffer, considering that the top of the pole is connected to the power suspension line. Thirdly, the horizontal projection method of the backbone area is utilized to eliminate the interference of vegetation in the buffer area. Finally, the point cloud of the telegraph pole is extracted through the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The experimental results demonstrate that the average values of Recall, Precision, and F1-score in telegraph pole detection can reach 91.09%, 90.82%, and 90.90%, respectively. The average RMSE value of location deviation is 0.51m. The average value of the F1-score in the telegraph pole extraction is 91.83%, and the average extraction time of a single pole is 0.27s. Accordingly, this method has strong adaptability to areas with lush vegetation and can automatically locate and extract the telegraph pole point cloud with high accuracy, and it can still achieve very high accuracy even under the holes in the data.

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A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds

Cited by 22 publications

References 14 publications

Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

Geometric Primitives in LiDAR Point Clouds: A Review

Location and Extraction of Telegraph Poles from Image Matching-Based Point Clouds

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