3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data

Su, Jingxin; Miyazaki, Ryuji; Tamaki, Toru; Kaneda, Kazufumi

doi:10.20965/ijat.2018.p0386

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…For the missing points between broken white lines, we describe the three-dimensional points in a length-angle space to fill the gap, considering that the road trajectories are sequences consisting of centerline points. Finally, we generate a 3D point sequence to represent the trajectory points [26], and hence we can use the trajectory of the road as the reference line in road surface modeling. Figure 4a,b shows examples of the results in our previous work.…”

Section: Input Data Preparationmentioning

confidence: 99%

“…However, the performance and accuracy of the two key steps-road segmentation and elevation estimation-can be further enhanced. In our previous work [25][26][27], we presented a workflow which can produce a high-precision three-dimensional point cloud model of a road surface region and trajectory points. In this paper, we extend the method based on our previous results for representing the mobile mapping data in the CRG model efficiently.…”

Section: Introductionmentioning

confidence: 99%

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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: Input Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Miyazaki

Tamaki

et al. 2019

Sensors

Self Cite

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Developing a Support System for Loading Planning

Nakamura¹,

Kaneko²,

Abe³

et al. 2019

IJAT

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While loading equipment into factories or facilities, pre-loading planning on computers to determine a loading object’s posture and its loading path will help reduce trials and errors at the site location. During such pre-loading planning, we need to detect the shape data of both the factory area and loading object and their interference states and then plan the loading object’s posture accordingly. In practice, however, there are a considerable number of issues while acquiring the latest factory area data and while grasping where the loading object in complex postures will result in interference, both simultaneously and for a short time period. In this study, we aimed at resolving the above-mentioned difficulties by developing a system that can detect where the loading object will interfere by means of polygonal shapes and can visualize the area by means of point clouds, both working in conjunction so that the developed system can automatically plan a loading object’s posture and can visualize its planned posture through augmented reality.

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Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

Ardilla

Saputra

Kubota

2023

IJAT

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Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.

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3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data

Cited by 3 publications

References 19 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

Developing a Support System for Loading Planning

Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

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