Fetching the paper…
Reading the bibliography…
To execute collaborative tasks in unknown environments, a robotic swarm needs to establish a global reference frame and locate itself in a shared understanding of the environment.
S. M. Prakhya, B. Liu, and W. Lin, “B-shot: A binary feature descriptor for fast and efficient keypoint matching on 3d point clouds,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2015, pp. 1929–1934
1934
Earlier work this paper cites.
M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures , vol. 1611. Spie, 1992, pp. 586–606
1992
Earlier work this paper cites.
Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on pattern analysis and machine intelligence , vol. 22, no. 11, pp. 1330–1334, 2000
2000
Earlier work this paper cites.
J. Knopp, M. Prasad, G. Willems, R. Timofte, and L. V. Gool, “Hough transform and 3d surf for robust three dimensional classification,” in European Conference on Computer Vision . Springer, 2010, pp. 589–602
2010
Earlier work this paper cites.
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. Cunningham, V. Indelman, and F. Dellaert, “Ddf-sam 2.0: Consistent distributed smoothing and mapping,” in 2013 IEEE international conference on robotics and automation . IEEE, 2013, pp. 5220–5227
2013
Earlier work this paper cites.
J. Maye, P. Furgale, and R. Siegwart, “Self-supervised calibration for robotic systems,” in 2013 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2013, pp. 473–480
2013
Earlier work this paper cites.
P. Furgale, J. Rehder, and R. Siegwart, “Unified temporal and spatial calibration for multi-sensor systems,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 1280–1286
2013
Earlier work this paper cites.
J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real-time.” in Robotics: Science and Systems , vol. 2, no. 9. Berkeley, CA, 2014, pp. 1–9
2014
Earlier work this paper cites.
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on robotics , vol. 32, no. 6, pp. 1309–1332, 2016
2016
Earlier work this paper cites.
S. Choudhary, L. Carlone, C. Nieto, J. Rogers, H. I. Christensen, and F. Dellaert, “Distributed trajectory estimation with privacy and communication constraints: a two-stage distributed gauss-seidel approach,” in IEEE International Conference on Robotics and Automation 2016 , 2016
2016
Earlier work this paper cites.
L. He, X. Wang, and H. Zhang, “M2dp: A novel 3d point cloud descriptor and its application in loop closure detection,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 231–237
2016
Earlier work this paper cites.
R. Tron, J. Thomas, G. Loianno, K. Daniilidis, and V. Kumar, “A distributed optimization framework for localization and formation control: Applications to vision-based measurements,” IEEE Control Systems Magazine , vol. 36, no. 4, pp. 22–44, 2016
2016
Earlier work this paper cites.
R. Dubé, A. Gawel, H. Sommer, J. Nieto, R. Siegwart, and C. Cadena, “An online multi-robot slam system for 3d lidars,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 1004–1011
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
T. Cieslewski and D. Scaramuzza, “Efficient decentralized visual place recognition using a distributed inverted index,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 640–647, 2017
2017
Earlier work this paper cites.
S. Choudhary, L. Carlone, C. Nieto, J. Rogers, H. I. Christensen, and F. Dellaert, “Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models,” The International Journal of Robotics Research , vol. 36, no. 12, pp. 1286–1311, 2017
2017
Earlier work this paper cites.
M. Grupp, “evo: Python package for the evaluation of odometry and slam.” https://github.com/MichaelGrupp/evo
2017
Cited alongside, same era.
J. G. Mangelson, D. Dominic, R. M. Eustice, and R. Vasudevan, “Pairwise consistent measurement set maximization for robust multi-robot map merging,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 2916–2923
2018
Cited alongside, same era.
M. Karrer, P. Schmuck, and M. Chli, “Cvi-slam–collaborative visual-inertial slam,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 2762–2769, 2018
2018
Cited alongside, same era.
K. P. Cop, P. V. Borges, and R. Dubé, “Delight: An efficient descriptor for global localisation using lidar intensities,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 3653–3660
2018
Cited alongside, same era.
T. Fan and T. Murphey, “Majorization minimization methods for distributed pose graph optimization with convergence guarantees,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5058–5065
2020
Later among the works it cites.
2021
Later among the works it cites.
D. Zhu, G. Xu, X. Wang, X. Liu, and D. Tian, “Paircon-slam: Distributed, online, and real-time rgbd-slam in large scenarios,” IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1–14, 2021
2021
Later among the works it cites.
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, 2021
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Kim and A. Kim, “Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 4802–4809
2018
Cited alongside, same era.
Z. J. Yew and G. H. Lee, “3dfeat-net: Weakly supervised local 3d features for point cloud registration,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 607–623
2018
Cited alongside, same era.
T. Cieslewski, S. Choudhary, and D. Scaramuzza, “Data-efficient decentralized visual slam,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 2466–2473
2018
Cited alongside, same era.
L. Zhou, Z. Li, and M. Kaess, “Automatic extrinsic calibration of a camera and a 3d lidar using line and plane correspondences,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 5562–5569
2018
Cited alongside, same era.
P. Schmuck and M. Chli, “Ccm-slam: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams,” Journal of Field Robotics , vol. 36, no. 4, pp. 763–781, 2019
2019
Cited alongside, same era.
W. Wang, N. Jadhav, P. Vohs, N. Hughes, M. Mazumder, and S. Gil, “Active rendezvous for multi-robot pose graph optimization using sensing over wi-fi,” in The International Symposium of Robotics Research . Springer, 2019, pp. 832–849
2019
Cited alongside, same era.
D. M. Rosen, L. Carlone, A. S. Bandeira, and J. J. Leonard, “Se-sync: A certifiably correct algorithm for synchronization over the special euclidean group,” The International Journal of Robotics Research , vol. 38, no. 2-3, pp. 95–125, 2019
2019
Cited alongside, same era.
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) . IEEE, 2020, pp. 5135–5142
2020
Cited alongside, same era.
Later among the works it cites.
Y. Chang, Y. Tian, J. P. How, and L. Carlone, “Kimera-multi: a system for distributed multi-robot metric-semantic simultaneous localization and mapping,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 11 210–11 218
2021
Later among the works it cites.
M. Ouyang, X. Shi, Y. Wang, Y. Tian, Y. Shen, D. Wang, P. Wang, and Z. Cao, “A collaborative visual slam framework for service robots,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8679–8685
2021
Later among the works it 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) . IEEE, 2021, pp. 171–176
2021
Later among the works it cites.
T. Shan, B. Englot, F. Duarte, C. Ratti, and D. Rus, “Robust place recognition using an imaging lidar,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 5469–5475
2021
Later among the works it cites.
K. Fischer, M. Simon, F. Olsner, S. Milz, H.-M. Gross, and P. Mader, “Stickypillars: Robust and efficient feature matching on point clouds using graph neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 313–323
2021
Later among the works it cites.
K. Koide, M. Yokozuka, S. Oishi, and A. Banno, “Voxelized gicp for fast and accurate 3d point cloud registration,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 11 054–11 059
2021
Later among the works it cites.
Y. Tian, K. Khosoussi, D. M. Rosen, and J. P. How, “Distributed certifiably correct pose-graph optimization,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 2137–2156, 2021
2021
Later among the works it cites.
W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar-inertial odometry,” IEEE Transactions on Robotics , 2022
2022
Closest in time.
S. Guo, Z. Rong, S. Wang, and Y. Wu, “A lidar slam with pca-based feature extraction and two-stage matching,” IEEE Transactions on Instrumentation and Measurement , vol. 71, pp. 1–11, 2022
2022
Closest in time.
J. Wang, M. Xu, G. Zhao, and Z. Chen, “Feature&distribution-based lidar slam with generalized feature representation and heuristic nonlinear optimization,” IEEE Transactions on Instrumentation and Measurement , 2022
2022
Closest in time.
2022
Closest in time.
T. Ye, X. Yan, S. Wang, Y. Li, and F. Zhou, “An efficient 3-d point cloud place recognition approach based on feature point extraction and transformer,” IEEE Transactions on Instrumentation and Measurement , vol. 71, pp. 1–9, 2022
2022
Closest in time.
L. Li, X. Kong, X. Zhao, T. Huang, W. Li, F. Wen, H. Zhang, and Y. Liu, “Ssc: Semantic scan context for large-scale place recognition,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 2092–2099
2099
Closest in time.