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Next generation map making: geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction

Published: 04 November 2009 Publication History

Abstract

This paper presents a novel method to process large scale, ground level Light Detection and Ranging (LIDAR) data to automatically detect geo-referenced navigation attributes (traffic signs and lane markings) corresponding to a collection travel path. A mobile data collection device is introduced. Both the intensity of the LIDAR light return and 3-D information of the point clouds are used to find retroreflective, painted objects. Panoramic and high definition images are registered with 3-D point clouds so that the content of the sign and color can subsequently be extracted.

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  • (2024)Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping DataRemote Sensing10.3390/rs1610166816:10(1668)Online publication date: 8-May-2024
  • (2024)On the Ecosystem of High-Definition (HD) Maps2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00010(40-47)Online publication date: 13-May-2024
  • (2024)Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA63115.2024.00092(600-607)Online publication date: 27-Nov-2024
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  1. Next generation map making: geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction

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      cover image ACM Conferences
      GIS '09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
      November 2009
      575 pages
      ISBN:9781605586496
      DOI:10.1145/1653771
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 04 November 2009

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      Author Tags

      1. LIDAR
      2. geo-reference
      3. ground-level
      4. lane marking
      5. retro-reflective
      6. road
      7. sign

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      Cited By

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      • (2024)Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping DataRemote Sensing10.3390/rs1610166816:10(1668)Online publication date: 8-May-2024
      • (2024)On the Ecosystem of High-Definition (HD) Maps2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00010(40-47)Online publication date: 13-May-2024
      • (2024)Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA63115.2024.00092(600-607)Online publication date: 27-Nov-2024
      • (2024)Automatic cross section extraction and cross slope measurement for curved ramps using light detection and ranging point cloudsMeasurement10.1016/j.measurement.2024.114369228(114369)Online publication date: Mar-2024
      • (2023)Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection TechniquesRemote Sensing10.3390/rs1519465615:19(4656)Online publication date: 22-Sep-2023
      • (2022)Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point CloudsTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812211064822677:2(372-384)Online publication date: 21-Jul-2022
      • (2022)Automated Annotation of Lane Markings Using LIDAR and OdometryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.303192123:4(3115-3125)Online publication date: Apr-2022
      • (2022)Robust Lane Extraction From MLS Point Clouds Towards HD Maps Especially in Curve RoadIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.302803323:2(1505-1518)Online publication date: Feb-2022
      • (2022)Traffic sign extraction using deep hierarchical feature learning and mobile light detection and ranging (LiDAR) data on rural highwaysJournal of Intelligent Transportation Systems10.1080/15472450.2022.207479227:5(643-664)Online publication date: 29-May-2022
      • (2022)Mobile Terrestrial Laser Scanning and MappingSurveying and Geomatics Engineering10.1061/9780784416037.ch9(303-340)Online publication date: 8-Jun-2022
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