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Clustering and visualization of non-classified points from LiDAR data for helicopter navigation

Clustering and visualization of non-classified points from LiDAR data for helicopter navigation

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EISENKEIL, Ferdinand, Tobias SCHAFHITZEL, Uwe KÜHNE, Oliver DEUSSEN, 2014. Clustering and visualization of non-classified points from LiDAR data for helicopter navigation. Sensor/Information Fusion, and Target Recognition XXIII. Baltimore, 5. Mai 2014 - 8. Mai 2014. In: KADAR, Ivan, ed.. Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII ; 5–8 May 2014 Baltimore, Maryland, United States. Sensor/Information Fusion, and Target Recognition XXIII. Baltimore, 5. Mai 2014 - 8. Mai 2014. Bellingham:SPIE, 90910V. ISBN 978-1-62841-028-0

@inproceedings{Eisenkeil2014Clust-30014, title={Clustering and visualization of non-classified points from LiDAR data for helicopter navigation}, year={2014}, doi={10.1117/12.2050497}, number={9091}, isbn={978-1-62841-028-0}, address={Bellingham}, publisher={SPIE}, series={Proceedings of SPIE}, booktitle={Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII ; 5–8 May 2014 Baltimore, Maryland, United States}, editor={Kadar, Ivan}, author={Eisenkeil, Ferdinand and Schafhitzel, Tobias and Kühne, Uwe and Deussen, Oliver}, note={Article Number: 90910V} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/30014"> <dc:creator>Schafhitzel, Tobias</dc:creator> <dcterms:abstract xml:lang="eng">In this paper we propose a dynamic DBSCAN-based method to cluster and visualize unclassified and potential dangerous obstacles in data sets recorded by a LiDAR sensor. The sensor delivers data sets in a short time interval, so a spatial superposition of multiple data sets is created. We use this superposition to create clusters incrementally. Knowledge about the position and size of each cluster is used to fuse clusters and the stabilization of clusters within multiple time frames. Cluster stability is a key feature to provide a smooth and un-distracting visualization for the pilot. Only a few lines are indicating the position of threatening unclassified points, where a hazardous situation for the helicopter could happen, if it comes too close. Clustering and visualization form a part of an entire synthetic vision processing chain, in which the LiDAR points support the generation of a real-time synthetic view of the environment</dcterms:abstract> <dc:creator>Eisenkeil, Ferdinand</dc:creator> <dc:creator>Kühne, Uwe</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30014"/> <dc:creator>Deussen, Oliver</dc:creator> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-02-25T07:12:06Z</dc:date> <dc:language>eng</dc:language> <dc:contributor>Schafhitzel, Tobias</dc:contributor> <dcterms:title>Clustering and visualization of non-classified points from LiDAR data for helicopter navigation</dcterms:title> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-02-25T07:12:06Z</dcterms:available> <dcterms:issued>2014</dcterms:issued> <dc:contributor>Eisenkeil, Ferdinand</dc:contributor> <dc:contributor>Kühne, Uwe</dc:contributor> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 25.02.2015 (Informationen über die Zugriffsstatistik)

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