Automatic lane marking extraction from point cloud into polygon map layer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F19%3A43914326" target="_blank" >RIV/62156489:43110/19:43914326 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26210/18:PU130002

Výsledek na webu

<a href="https://doi.org/10.1080/22797254.2018.1535837" target="_blank" >https://doi.org/10.1080/22797254.2018.1535837</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/22797254.2018.1535837" target="_blank" >10.1080/22797254.2018.1535837</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic lane marking extraction from point cloud into polygon map layer

Popis výsledku v původním jazyce

Optimization of road networks is a common concern worldwide, primarily for safety purposes. Because the extent of these networks is substantial, automation of their inventory is highly desirable. This paper concentrates on the road inventory process that is necessary for regular maintenance. The key part of our road marking detection and reconstruction is based on spanning tree usage. The spanning trees are obtained from alpha shapes of the detected road markings. The spanning trees application enables the reliable identification of the road markings and precise reconstruction of their contours even with noisy data. Our method processes the point cloud data obtained from LiDAR measurements, and provides a common vector layer with road lane polygons. Such a vector layer is stored in a common file format supported by the majority of geographical information systems, thus producing an output that can be conveniently used for decision-making based on the road inventory process.

Název v anglickém jazyce

Automatic lane marking extraction from point cloud into polygon map layer

Popis výsledku anglicky

Optimization of road networks is a common concern worldwide, primarily for safety purposes. Because the extent of these networks is substantial, automation of their inventory is highly desirable. This paper concentrates on the road inventory process that is necessary for regular maintenance. The key part of our road marking detection and reconstruction is based on spanning tree usage. The spanning trees are obtained from alpha shapes of the detected road markings. The spanning trees application enables the reliable identification of the road markings and precise reconstruction of their contours even with noisy data. Our method processes the point cloud data obtained from LiDAR measurements, and provides a common vector layer with road lane polygons. Such a vector layer is stored in a common file format supported by the majority of geographical information systems, thus producing an output that can be conveniently used for decision-making based on the road inventory process.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Journal of Remote Sensing

ISSN

2279-7254

e-ISSN

—

Svazek periodika

52

Číslo periodika v rámci svazku

S1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

14

Strana od-do

26-39

Kód UT WoS článku

000475928900004

EID výsledku v databázi Scopus

2-s2.0-85055685908