Recognizing outdoor scenes by convolutional features of omni-directional LiDAR scans

Kazuto Nakashima, Seungwoo Nham, Hojung Jung, Yumi Iwashita, Ryo Kurazume, Oscar M. Mozos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel method for the outdoor scene categorization using 2D convolutional neural networks (CNNs) which take panoramic depth images obtained by a 3D laser scanner as input. We evaluate our approach in two outdoor scene datasets including six categories: coast, forest, indoor parking, outdoor parking, residential area, and urban area. Our results on both datasets (over 94%) outperform previous approaches and show the effectiveness of this approach for outdoor scene categorization using depth images. To analyze our trained networks we visualize the learned features by using two visualization methods.

Original languageEnglish
Title of host publicationSII 2017 - 2017 IEEE/SICE International Symposium on System Integration
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages387-392
Number of pages6
Volume2018-January
ISBN (Electronic)9781538622636
DOIs
Publication statusPublished - Feb 1 2018
Event2017 IEEE/SICE International Symposium on System Integration, SII 2017 - Taipei, Taiwan, Province of China
Duration: Dec 11 2017Dec 14 2017

Publication series

NameSII 2017 - 2017 IEEE/SICE International Symposium on System Integration
Volume2018-January

Conference

Conference2017 IEEE/SICE International Symposium on System Integration, SII 2017
CountryTaiwan, Province of China
CityTaipei
Period12/11/1712/14/17

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All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Science Applications
  • Engineering (miscellaneous)
  • Materials Science (miscellaneous)
  • Control and Optimization

Cite this

Nakashima, K., Nham, S., Jung, H., Iwashita, Y., Kurazume, R., & Mozos, O. M. (2018). Recognizing outdoor scenes by convolutional features of omni-directional LiDAR scans. In SII 2017 - 2017 IEEE/SICE International Symposium on System Integration (Vol. 2018-January, pp. 387-392). (SII 2017 - 2017 IEEE/SICE International Symposium on System Integration; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SII.2017.8279243