Mobile Terrestrial Laser Scanning for Highway Inventory Data Collection

Gong, Jianwei; Zhou, Huaguo; Gordon, Chris; Jalayer, Mohammad

doi:10.1061/9780784412343.0069

Cited by 45 publications

(35 citation statements)

References 4 publications

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“…The average errors in this study were relatively close to the errors and even near accuracies found in other studies in which LiDAR laser scanning was used for geotechnical applications [26,55]. Su et al [55] mentioned root mean square errors within the range of 4-19 mm, whereas this study presented a mean error range of 3.3-20.9 mm for the tested scenarios.…”

Section: Discussionmentioning

confidence: 40%

“…Su et al [55] mentioned root mean square errors within the range of 4-19 mm, whereas this study presented a mean error range of 3.3-20.9 mm for the tested scenarios. Gong et al [26] noted accuracies below 10 mm, as this study had an average range minimum value near 10 mm, but also acknowledges the different setups and displacement scales that were used. Similar results were noted in comparing Oskouie et al's [56] results of maximum displacement errors near 2.4 mm, but encompassed different collection distance and a single panel subjected to simulated movement.…”

Section: Discussionmentioning

confidence: 99%

“…Decreased costs and high spatial resolution of remote sensing tools, such as Light Detection and Ranging (LiDAR), laser scanning (terrestrial), and Synthetic Aperture Radar (SAR), have shown promising results for detecting structure changes [17,[26][27][28][29][30][31]. On the other hand, there are some limitations, including attaining suitable atmospheric conditions, acquiring target setups for measurements, and accessing satellite availability for devices, which can become a significant challenge [17,[26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Application of Photogrammetry for Retaining Wall Assessment

2017

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Abstract:Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1-3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.

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Section: Discussionmentioning

confidence: 40%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Application of Photogrammetry for Retaining Wall Assessment

2017

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“…In addition to feature extraction, they also demonstrate the ability of software to automatically detect road signs and classify them by shape as defined by the Manual on Uniform Traffic Control Devices (MUTCD). Increases in safety, as well as the speed of MLS data collection for use as an inventory mapping tool, have encouraged many DOTs to adopt MLS technology [70].…”

Section: Inventory Mappingmentioning

confidence: 99%

Synthesis of Transportation Applications of Mobile LIDAR

et al. 2013

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A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on "what" is being done in practice, but not a lot on "how" and "how well" it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR.

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“…From these categories, as-built data sensing and visualization are considered by many industry practitioners and academia experts as one of the most promising technologies that will greatly expand the utilization of site information [1] [2]. For instance, 3D point clouds produced by laser scanners and other data acquisition technologies have been widely used for acquiring and generating as-built site information to support applications such as construction quality assessment and control [3] [4], construction progress tracking [5][6], building energy analysis [7], construction hazard recognition [8], structural health monitoring [9] [10] and highway asset management [11] [12]. Each application has a different focus of target objects (e.g., building components, materials, equipment, workers, and traffic signs) on the as-built point clouds.…”

Section: Introductionmentioning

confidence: 99%

Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

Chen¹,

Fang²,

Cho³

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Recognizing construction assets from as-is point cloud data of construction environment provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study proposes a fast and automated processing pipeline for construction target assets recognition from scattered as-is point clouds. The recognition tasks can be subdivided into object detection, which involves computing the bounding box around each construction equipment, and object classification, which involves labelling point cloud clusters from discrete equipment categories, such as backhoe loader, bulldozer, dump truck, excavator and front loader. The object detection step consists of point cloud down-sampling, segmentation and clustering. For the object classification step, machine learning methods were employed to determine class membership probability using features derived from the ESF (Ensemble of Shape Functions) descriptor. The classifiers were trained on synthetic point clouds generated from CAD (Computer-aided Design) models. The method was validated using laser scanned point clouds from an equipment yard. The test results demonstrate promising advancements towards semantic labelling and scene understanding of point cloud data.

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Mobile Terrestrial Laser Scanning for Highway Inventory Data Collection

Cited by 45 publications

References 4 publications

A Novel Application of Photogrammetry for Retaining Wall Assessment

A Novel Application of Photogrammetry for Retaining Wall Assessment

Synthesis of Transportation Applications of Mobile LIDAR

Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

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