A File Structure and Reference Data Set for High Resolution Hyperspectral 3D Point Clouds
Autor(en): | Wiemann, T. Igelbrink, F. Pütz, S. Hertzberg, J. |
Herausgeber: | Wiszniewski, B. Kowalczuk, Z. Domzalski, M. |
Stichwörter: | 3D Mapping; Data Storage; Digital storage; Hyperspectral Imaging; Information; Mapping; Perception; Remote sensing; Robots; Scanning; Sensing; Sensor data fusion; Sensor Fusion; Sensor fusion, Hyperspectral imaging; Sensory perception; Spectroscopy, 3-D mapping | Erscheinungsdatum: | 2019 | Herausgeber: | Elsevier B.V. | Journal: | IFAC-PapersOnLine | Volumen: | 52 | Ausgabe: | 8 | Startseite: | 93 | Seitenende: | 98 | Zusammenfassung: | Hyperspectral imaging has been extensively studied in remote sensing. In this community, several approaches exist for classifying different organic and an-organic materials. However, this data is usually collected from large distances (flight or satellite data) and hence lacks geometric precision, which is required for robotic applications like mapping and navigation. In this paper, we present a reference data set that maps hyperspectral intensity data to a terrestrial 3D laser scanner to generate what we call hyperspectral point clouds (HPCs). To organize and distribute the resulting massive data, we designed an HDF5 file structure that is the basis to feed information derived from the raw data into robot control frameworks like ROS. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. |
Beschreibung: | Conference of 10th IFAC Symposium on Intelligent Autonomous Vehicles, IAV 2019 ; Conference Date: 3 July 2019 Through 5 July 2019; Conference Code:141398 |
ISSN: | 24058963 | DOI: | 10.1016/j.ifacol.2019.08.101 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074387261&doi=10.1016%2fj.ifacol.2019.08.101&partnerID=40&md5=8d422bf2d445d34733511a3bd297ab53 |
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geprüft am 21.05.2024