Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/92766
PIRA download icon_1.1View/Download Full Text
Title: UrbanLoco : a full sensor suite dataset for mapping and localization in urban scenes
Authors: Wen, W 
Zhou, Y
Zhang, G 
FahandezhSaadi, S
Bai, X 
Zhan, W
Tomizuka, M
Hsu, LT 
Issue Date: 2020
Source: 2020 IEEE International Conference on Robotics and Automation (ICRA), 31 May-31 Aug. 2020, Paris, France, p. 2310 - 2316
Abstract: Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multi-path problems; the inertia measurement units (IMU) suffer from drifting. Unfortunately, current public datasets either do not adequately address this urban challenge or do not provide enough sensor information related to map-ping and localization. Here we present UrbanLoco: a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers. Our dataset includes a wide variety of urban terrains: urban canyons, bridges, tunnels, sharp turns, etc. More importantly, our dataset includes information from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly available through the link in the footnote 1.
Publisher: IEEE Computer Society
ISBN: 978-1-7281-7395-5 (Electronic ISBN)
978-1-7281-7394-8 (USB ISBN)
978-1-7281-7396-2 (Print on Demand(PoD) ISBN)
DOI: 10.1109/ICRA40945.2020.9196526
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Wen, W., Zhou, Y., Zhang, G., Fahandezh-Saadi, S., Bai, X., Zhan, W., ... & Hsu, L. T. (2020, May). Urbanloco: A full sensor suite dataset for mapping and localization in urban scenes. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2310-2316). IEEE is available at https://doi.org/10.1109/ICRA40945.2020.9196526
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Wen_Urbanloco.pdfPre-Published version6.57 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

6
Citations as of Jun 26, 2022

Downloads

5
Citations as of Jun 26, 2022

SCOPUSTM   
Citations

19
Citations as of Jun 23, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.