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Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

Hillemann, Markus; Weinmann, Martin; Mueller, Markus S.; Jutzi, Boris

Abstract:
Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000098381
Veröffentlicht am 20.09.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000098381
Erschienen in Remote sensing
Band 11
Heft 16
Seiten Article: 1955
Vorab online veröffentlicht am 20.08.2019
Schlagworte mobile mapping; laser scanning; self-calibration; 3D point clouds; geometric features
Nachgewiesen in Web of Science
Scopus
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