Accurate object localization in 3D laser range scans
| Autor(en): | Nüchter, A. Lingemann, K. Hertzberg, J. Surmann, H. |
Stichwörter: | Classification (of information); Data acquisition; Image processing; Lasers; Learning systems; Mobile robots, Ada Boost learning; Classification and regression trees (CARTs); Object localization, Object recognition | Erscheinungsdatum: | 2005 | Journal: | 2005 International Conference on Advanced Robotics, ICAR '05, Proceedings | Volumen: | 2005 | Startseite: | 665 | Seitenende: | 672 | Zusammenfassung: | This paper presents a novel method for object detection and classification in 3D laser range data that is acquired by an autonomous mobile robot. Unrestricted objects are learned using classification and regression trees (CARTs) and using an Ada Boost learning procedure. Off-screen rendered depth and reflectance images serve as an input for learning. The performance of the classification is improved by combining both sensor modalities, which are independent from external light This enables highly accurate, fast and reliable 3D object localization with point matching. Competitive learning is used for evaluating the accuracy of the object localization. © 2005 IEEE. |
Beschreibung: | Conference of 12th International Conference on Advanced Robotics, 2005. ICAR '05 ; Conference Date: 18 July 2005 Through 20 July 2005; Conference Code:67859 |
ISBN: | 9780780391772 | DOI: | 10.1109/ICAR.2005.1507480 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-33749050161&doi=10.1109%2fICAR.2005.1507480&partnerID=40&md5=12630b907c1d6e56eea48fe5fca39be2 |
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