Multi-modal panoramic 3D outdoor datasets for place categorization
Hojung Jung, Yuki Oto, Oscar M. Mozos, Yumi Iwashita, Ryo Kurazume
Abstract
We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).
BibTeX
@inproceedings{iros2016_multimodalpanora,
title = {Multi-modal panoramic 3D outdoor datasets for place categorization},
author = {Hojung Jung and Yuki Oto and Oscar M. Mozos and Yumi Iwashita and Ryo Kurazume},
booktitle = {IROS 2016},
year = {2016}
}