Paper Info
Title
Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization.
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.
Year
DOI
Venue
2018
10.1080/01691864.2018.1501279
ADVANCED ROBOTICS
Keywords
Field
DocType
Outdoor place categorization,convolutional neural networks,multi-modal data,laser scanner
Computer vision,Omnidirectional antenna,Categorization,Laser scanning,Convolutional neural network,Residential area,Control engineering,Lidar,Artificial intelligence,Engineering,Robot,Point cloud
Journal
Volume
Issue
ISSN
32.0
14
0169-1864
Citations 
PageRank 
References 
0
0.34
21
Authors
6
Name
Order
Citations
PageRank
Kazuto Nakashima113.14
Hojung Jung282.51
Yuki Oto300.34
Yumi Iwashita421223.59
Ryo Kurazume562274.18
Óscar Martínez Mozos642925.82