Multi-modal panoramic 3D outdoor datasets for place categorization

Hojung Jung, Yuki Oto, Oscar M. Mozos, Yumi Iwashita, Ryo Kurazume

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

4 Citations (Scopus)

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).

Original languageEnglish
Title of host publicationIROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4545-4550
Number of pages6
Volume2016-November
ISBN (Electronic)9781509037629
DOIs
Publication statusPublished - Nov 28 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
Duration: Oct 9 2016Oct 14 2016

Other

Other2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
CountryKorea, Republic of
CityDaejeon
Period10/9/1610/14/16

Fingerprint

Semantics
Color
Lasers
Parking
Coastal zones
Railroad cars

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Jung, H., Oto, Y., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2016). Multi-modal panoramic 3D outdoor datasets for place categorization. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 2016-November, pp. 4545-4550). [7759669] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2016.7759669

Multi-modal panoramic 3D outdoor datasets for place categorization. / Jung, Hojung; Oto, Yuki; Mozos, Oscar M.; Iwashita, Yumi; Kurazume, Ryo.

IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2016-November Institute of Electrical and Electronics Engineers Inc., 2016. p. 4545-4550 7759669.

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

Jung, H, Oto, Y, Mozos, OM, Iwashita, Y & Kurazume, R 2016, Multi-modal panoramic 3D outdoor datasets for place categorization. in IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 2016-November, 7759669, Institute of Electrical and Electronics Engineers Inc., pp. 4545-4550, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, Korea, Republic of, 10/9/16. https://doi.org/10.1109/IROS.2016.7759669
Jung H, Oto Y, Mozos OM, Iwashita Y, Kurazume R. Multi-modal panoramic 3D outdoor datasets for place categorization. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2016-November. Institute of Electrical and Electronics Engineers Inc. 2016. p. 4545-4550. 7759669 https://doi.org/10.1109/IROS.2016.7759669
Jung, Hojung ; Oto, Yuki ; Mozos, Oscar M. ; Iwashita, Yumi ; Kurazume, Ryo. / Multi-modal panoramic 3D outdoor datasets for place categorization. IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2016-November Institute of Electrical and Electronics Engineers Inc., 2016. pp. 4545-4550
@inproceedings{34593de99810408692e741e9e394f715,
title = "Multi-modal panoramic 3D outdoor datasets for place categorization",
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).",
author = "Hojung Jung and Yuki Oto and Mozos, {Oscar M.} and Yumi Iwashita and Ryo Kurazume",
year = "2016",
month = "11",
day = "28",
doi = "10.1109/IROS.2016.7759669",
language = "English",
volume = "2016-November",
pages = "4545--4550",
booktitle = "IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Multi-modal panoramic 3D outdoor datasets for place categorization

AU - Jung, Hojung

AU - Oto, Yuki

AU - Mozos, Oscar M.

AU - Iwashita, Yumi

AU - Kurazume, Ryo

PY - 2016/11/28

Y1 - 2016/11/28

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=85006365104&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006365104&partnerID=8YFLogxK

U2 - 10.1109/IROS.2016.7759669

DO - 10.1109/IROS.2016.7759669

M3 - Conference contribution

VL - 2016-November

SP - 4545

EP - 4550

BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -