The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Open accessAbstract
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 footnote1.
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Cites
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632
Two years of Visual Odometry on the Mars Exploration Rovers
Maimone, M., Cheng, Y., & Matthies, L. (2007) · Journal of Field Robotics · doi:10.1002/rob.20184 · Cited by 655 · cited 1×
“Vision-based Mapping and Localization Stereo cameras were used to extract spatial information about a scene [24] [22], but it is hard for monocular camera to solve the general motion.”
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Cited by
Bai, C., Xiao, T., Chen, Y., Wang, H., Zhang, F., & Gao, X. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3152830 · Cited by 389
Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Wen, W., Pfeifer, T., Bai, X., & Hsu, L. (2021) · NAVIGATION · doi:10.1002/navi.421 · Cited by 259
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Metadata
- Authors
- Wen, W., Zhou, Y., Zhang, G., Fahandezh-Saadi, S., Bai, X., Zhan, W., Tomizuka, M., & Hsu, L.-T.
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 2310-2316
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3089389996
- Cited by
- 172
- References
- 5
- FWCI
- 29.30
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wen, W., Zhou, Y., Zhang, G., Fahandezh-Saadi, S., Bai, X., Zhan, W., Tomizuka, M., & Hsu, L.-T. (2020). UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes. 2020 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra40945.2020.9196526
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