Automated extraction of manhole covers using mobile LiDAR data

Guan, Haiyan; Yu, Yongtao; Li, Jonathan; Liu, Pengfei; Zhao, Haohao; Wang, Cheng

doi:10.1080/2150704x.2014.994716

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…Due to manhole covers being located on road surfaces, several manhole cover extraction algorithms were performed on 2D raster data interpolated from 3D mobile LiDAR points. Guan et al (2014b) extracted manhole covers by applying distance-dependent thresholding, multi-scale tensor voting and morphological operations to the interpolated feature images. Similarly, Yu et al (2015c) developed a supervised deep learning model to depict high-order features of local image patches, and a random forest model to locate urban road manhole covers.…”

Section: Road Surface Structuresmentioning

confidence: 99%

Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

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Mobile LiDAR technology is currently one of the attractive topics in the fields of remote sensing and laser scanning. Mobile LiDAR enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced data, in the form of three-dimensional (3D) point clouds. This technology has been gaining popularity in the recognition of roads and road-scene objects. A thorough review of available literature is conducted to inform the advancements in mobile LiDAR technologies and their applications in road information inventory. The literature review starts with a brief overview of mobile LiDAR technology, including system components, direct geo-referencing, data error analysis and geometrical accuracy validation. Then, this review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and polelike objects. Finally, the challenges and future trends are discussed. Our review demonstrates the great potential of mobile LiDAR technology in road information inventory. ARTICLE HISTORY

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Section: Road Surface Structuresmentioning

confidence: 99%

Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

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174

119

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“…Meanwhile, MLS point clouds have also been widely used for road manhole cover detection. Guan et al [91] proposed an MSTV algorithm to detect road manhole covers from MLS point clouds. First, ground points were extracted and rasterized into 2D GRF images.…”

Section: Road Manhole Detectionmentioning

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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“…The assessment is carried out at point cloud level. Analysis results are shown in Table 5, at the point cloud level, precision, accuracy and sensitivity are computed using Equations (12)- (14).…”

Section: Quantitative Evaluation Of Rail Extractionmentioning

confidence: 99%

“…The on-road points occupy the biggest part of MLS points in road. Extraction methods [2,[9][10][11][12][13][14] aimed at detecting and extracting on-road objects (e.g., driving lines, road boundaries, road cracks and road manholes) performed well both in accuracy and precision. The off-road points could be used to identify and extract traffic signs, trees, power lines and light poles, holes, and cracks in sidewalks.…”

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confidence: 99%

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

et al. 2018

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Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required. terrestrial laser scanning (TLS), and MLS is common in recent years because laser scanners provide massive high-quality range measurements with intensity features in a fast manner. Many remarkable practices are made in MLS, ALS, and TLS. The ALS technology has been commercialized in the generation of high-precision digital elevation and surface models [3]. The first MMS [4] used onboard satellite receivers and stereo cameras to capture road images. At that time, precise point positioning [5] technology and differential positioning [6] technology were not introduced yet. IMUs are expensive and the application of mobile mapping systems (MMSs) is limited. Currently, Crucial high-end MMSs such as the Optech Lynx mobile mapping system, Trimble MX-9 MLS system, and RIGEL VMX-2HA MLS system could capture over millions of points every second. Commercial MMSs are applied to point cloud data acquisition of urban road networks and railroad corridors. However, commercial MMSs do not provide a user programming interface, which makes object detection and extraction applications based on them impossible. Consequently, previous studies were focused on the combined use of those devices [7].In general terms, an MLS platform is a vehicle-mounted MMS that is integrated with multiple sensors (e.g., GNSS antenna, IMU, digital cameras) and equipped with a centralized computing system for data synchronization and management. Existing urban reconstruction algorithms using data from different source (e.g., ALS, TLS, MLS) obtain convincing results in urban object modeling and management. Data collected by vehicle-mounted MMSs can be used to detect o...

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Automated extraction of manhole covers using mobile LiDAR data

Cited by 18 publications

References 10 publications

Use of mobile LiDAR in road information inventory: a review

Use of mobile LiDAR in road information inventory: a review

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

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

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