Research for Generating Road Alignment of Highway Bridges with Point Cloud Data using MMS

Tanaka, Shigenori; Nakamura, Kenji; Yamamoto, Yukari; Imai, Ryuichi; Kubota, Satoshi; Jiang, Wenyuan

doi:10.3156/jsoft.28.826

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Since the geometric relationships between road segments are normally assumed as straight line segments and curved arc segments, there are many different approaches that are available, dealing with various situations. Examples can be found in [18][19][20][21]. In geometric road design, G2 continuity is demanded, which means that not only the tangent vectors between different road segments are lying along the same direction, but also have the same curvature at the joint point.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Miyazaki

Tamaki

et al. 2019

Sensors

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As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle simulations. In previous research, we presented our proposed method that can create a precise three-dimensional point cloud model of road surface regions and trajectory points. Our data sets were obtained by a vehicle-mounted mobile mapping system (MMS). The collected data were converted into point cloud data and color images. In this paper, we utilize the previous results as input data and present a solution that can generate an elevation grid for building an OpenCRG model. The OpenCRG project was originally developed to describe road surface elevation data, and also defined an open file format. As it can be difficult to generate a regular grid from point cloud directly, the road surface is first divided into straight lines, circular arcs, and and clothoids. Secondly, a non-regular grid which contains the elevation of road surface points is created for each road surface segment. Then, a regular grid is generated by accurately interpolating the elevation values from the non-regular grid. Finally, the curved regular grid (CRG) model files are created based on the above procedures, and can be visualized by OpenCRG tools. The experimental results on real-world data show that the proposed approach provided a very-high-resolution road surface elevation model.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Miyazaki

Tamaki

et al. 2019

Sensors

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Research on Identification of Road Features from Point Cloud Data Using Deep Learning

Umehara

Tsukada

Nakamura

et al. 2021

IJAT

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Laser measurement technology has progressed significantly in recent years, and diverse methods have been developed to measure three-dimensional (3D) objects within environmental spaces in the form of point cloud data. Although such point cloud data are expected to be used in a variety of applications, such data do not possess information on the specific features represented by the points, making it necessary to manually select the target features. Therefore, the identification of road features is essential for the efficient management of point cloud data. As a technology for identifying features from the point cloud data of road spaces, in this research, we propose a method for automatically dividing point cloud data into units of features and identifying features from projected images with added depth information. We experimentally verified that the proposed method accurately identifies and extracts such features.

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Research for Extracting Road Earthwork Structures Using Point Cloud Data by MMS and Laser Profiler

Nakamura¹,

Tsukada²,

UMEHARA³

et al. 2022

J. JSCE, Ser. F3

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Research for Generating Road Alignment of Highway Bridges with Point Cloud Data using MMS

Cited by 3 publications

References 11 publications

High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Research on Identification of Road Features from Point Cloud Data Using Deep Learning

Research for Extracting Road Earthwork Structures Using Point Cloud Data by MMS and Laser Profiler

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