Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping

Jaakkola, Anttoni; Hyyppä, Juha; Hyyppä, Hannu; Kukko, Antero

doi:10.3390/s8095238

Cited by 182 publications

(135 citation statements)

References 5 publications

(4 reference statements)

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“…Currently, certain methods based on MLS data are also available: Goulette et al [31], Kukko [32], Jaakkola et al [33] and Pu et al [21]. MLS provides direct information to acquire the positions of railroads if the trajectory of the MLS fits the center line of one of the railroads.…”

Section: (I) Building Extraction and Reconstructionmentioning

confidence: 99%

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

Zhu

Hyyppä

2014

Remote Sensing

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This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution.

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Section: (I) Building Extraction and Reconstructionmentioning

confidence: 99%

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

Zhu

Hyyppä

2014

Remote Sensing

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“…Precise extraction of road markings from LiDAR data has drawn limited attention from the research community. Jaakkola et al (2008) estimated road markings by first performing a radiometric correction of the LiDAR intensity data using a second order curve fitting function. Finally, road markings were estimated by applying a threshold and morphological filtering methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

105

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a b s t r a c tRoad markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

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“…MLS has been used for modeling road surfaces [31], extracting building facades [29,[32][33][34], and detecting trees, streetlights, and other pole-like objects [28,[35][36][37]. Previous studies have demonstrated the great potential of MLS for street objects identification.…”

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confidence: 99%

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

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Abstract:As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

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Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping

Cited by 182 publications

References 5 publications

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

Automated road markings extraction from mobile laser scanning data

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

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