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

Kazuto Nakashima, Hojung Jung, Yuki Oto, Yumi Iwashita, Ryo Kurazume, Oscar Martinez Mozos

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over 24 hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks, we visualize the learned features.

Original languageEnglish
Pages (from-to)750-765
Number of pages16
JournalAdvanced Robotics
Volume32
Issue number14
DOIs
Publication statusAccepted/In press - Jan 1 2018

All Science Journal Classification (ASJC) codes

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

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