S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM

K. Y. Leung, Y. Halpern, T. D. Barfoot, and H. H. Liu, “The utias multi-robot cooperative localization and mapping dataset,” The International Journal of Robotics Research , vol. 30, no. 8, pp. 969–974, 2011

2011

Earlier work this paper cites.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013

2013

Earlier work this paper cites.

M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, “The euroc micro aerial vehicle datasets,” The International Journal of Robotics Research , vol. 35, no. 10, pp. 1157–1163, 2016

2016

Earlier work this paper cites.

T. Qin, J. Pan, S. Cao, and S. Shen, “A general optimization-based framework for local odometry estimation with multiple sensors,” arXiv preprint arXiv:1901.03638 , 2019

Original

2019

Earlier work this paper cites.

S. Agarwal, A. Vora, G. Pandey, W. Williams, H. Kourous, and J. McBride, “Ford multi-av seasonal dataset,” The International Journal of Robotics Research , vol. 39, no. 12, pp. 1367–1376, 2020

2020

Earlier work this paper cites.

R. Dubois, A. Eudes, and V. Frémont, “Airmuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial simultaneous localization and mapping,” in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) , Sep. 2020, pp. 166–172

2020

Earlier work this paper cites.

T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct 2020, pp. 5135–5142

2020

Earlier work this paper cites.

P. Schmuck, T. Ziegler, M. Karrer, J. Perraudin, and M. Chli, “Covins: Visual-inertial slam for centralized collaboration,” in 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) , Oct 2021, pp. 171–176

2021

Cited alongside, same era.

C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. M. Montiel, and J. D. Tardós, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, Dec 2021

2021

Cited alongside, same era.

T. Shan, B. Englot, C. Ratti, and D. Rus, “Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , May 2021, pp. 5692–5698

2021

Cited alongside, same era.

Y. Huang, T. Shan, F. Chen, and B. Englot, “Disco-slam: Distributed scan context-enabled multi-robot lidar slam with two-stage global-local graph optimization,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1150–1157, April 2022

S. Zhong, Y. Qi, Z. Chen, J. Wu, H. Chen, and M. Liu, “Dcl-slam: A distributed collaborative lidar slam framework for a robotic swarm,” IEEE Sensors Journal , 2023

2023

Closest in time.

Y. Tian, Y. Chang, L. Quang, A. Schang, C. Nieto-Granda, J. P. How, and L. Carlone, “Resilient and distributed multi-robot visual slam: Datasets, experiments, and lessons learned,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 11 027–11 034

2023

Closest in time.

Y. Zhu, Y. Kong, Y. Jie, S. Xu, and H. Cheng, “Graco: A multimodal dataset for ground and aerial cooperative localization and mapping,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 966–973, 2023

2023

Closest in time.

P.-Y. Lajoie and G. Beltrame, “Swarm-slam: Sparse decentralized collaborative simultaneous localization and mapping framework for multi-robot systems,” IEEE Robotics and Automation Letters , vol. 9, no. 1, pp. 475–482, 2023

2023