Road Curb Extraction From Mobile LiDAR Point Clouds

Xu, Sheng; Wang, Rui-Sheng; Zheng, Han

doi:10.1109/tgrs.2016.2617819

Cited by 68 publications

(31 citation statements)

References 22 publications

(30 reference statements)

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“…Each step is explained in detail in the following sub-sections. Xu et al [17] present a two-step solution to extract 3D curb edges based on the point cloud roughness. In the first step, candidate points of curbs are identified using a proposed novel energy function.…”

Section: Methodsmentioning

confidence: 99%

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Gézero

Antunes

2019

IJGI

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In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.Typically, when obtained using photogrammetric methods or a field survey, the break line designs are time consuming.Mobile Light Detection And Ranging (LiDAR) is a widely disseminated technology that allows for gathering dense and precise point clouds. Initially installed on aerial platforms, the static and mobile terrestrial systems rapidly emerged, becoming a crucial source of georeferenced data. However, LiDAR is a non-selective technique, i.e., the georeferenced point clouds represent the sensor's surrounding physical reality at an acquisition moment, indiscriminately, with no classification of terrain, vegetation, buildings, or any other object within the system range. The automatic classification and object extraction from those point clouds became a noteworthy challenge for researchers.Since the initial LiDAR systems, numerous techniques for automatic break lines extraction have been presented. Brugelmann [6] shortly describes some methods for break lines extraction from airborne point clouds, testing an approach proposed by Forstner [7]. In that approach, all pixels with a significant noise homogeneity measure are denoted as potential edge pixels. The quadratic variation, used as a homogeneity measure, indicates the extent of the curvature. Although the resulting lines have a lower quality when compared with the photogrammetrically measured break lines, the author showed that automatic break line extraction from airborne laser data is feasible.Other approach based on two intersecting planes as fitted functions can extract some 3D lines, but they nee...

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Section: Methodsmentioning

confidence: 99%

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Gézero

Antunes

2019

IJGI

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“…Guan et al [38] proposed a curb-based road extraction method to separate pavement surfaces from roadsides with the assumption that road curbstones can represent the boundaries of pavement. Xu et al [39] proposed an automated road curb extraction method using MLS point clouds. First, candidates' points of pavement curbs were extracted by an energy function.…”

Section: D Point-based Extractionmentioning

confidence: 99%

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

et al. 2018

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The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

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“…Noteworthy that these transform estimators has been successfully used in many remote sensing tasks, such as fundamental matrix estimation with only translation and radial distortion [45], multi-modal correspondence [46], Quasi-Homography transform estimation for wide baseline stereo [47], and rotation-scaling-translation estimation based on fractal image model [48]. In particular, robust estimators are suitable for the model fitting task in point clouds, such as plane and roof reconstruction [49][50][51], road fitting and segmentation [52,53], etc. Apart from the "fit-and-remove" scheme, there are several strategies for estimating transformation.…”

Section: Related Workmentioning

confidence: 99%

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

Cai

Leng

et al. 2018

Remote Sensing

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Abstract:Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV) low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW), Sliding Window (SW), Reverse Residual Order (RRO), respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change.

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Road Curb Extraction From Mobile LiDAR Point Clouds

Cited by 68 publications

References 22 publications

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

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