Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization-Reference-Cited by-同舟云学术

Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization

Published:2018-07-18 Issue:14 Volume:32 Page:750-765
ISSN:0169-1864
Container-title:Advanced Robotics
language:en
Short-container-title:Advanced Robotics

Author:

Nakashima Kazuto¹^ORCID,Jung Hojung¹^ORCID,Oto Yuki¹,Iwashita Yumi²,Kurazume Ryo³^ORCID,Mozos Oscar Martinez⁴

Affiliation:

1. Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

3. Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

4. Technical University of Cartagena, Cartagena, Spain

Funder

Japan Society for the Promotion of Science

Publisher

Informa UK Limited

Subject

Computer Science Applications,Hardware and Architecture,Human-Computer Interaction,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/01691864.2018.1501279

Reference42 articles.

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3. Kollar T, Roy N. Utilizing object-object and object-scene context when planning to find things. In: Proceedings of the IEEE international conference on robotics and automation (ICRA); IEEE; 2009. p. 2168–2173

4. Pronobis A, Jensfelt P, Sjöö K, et al. Semantic modelling of space. In: Cognitive systems. Berlin: Springer; 2010. p. 165–221

5. Cognitive Systems

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