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We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness.
Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image and vision Computing , vol. 15, no. 1, pp. 59–76, 1997
1997
Earlier work this paper cites.
P. H. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer vision and image understanding , vol. 78, no. 1, pp. 138–156, 2000
2000
Earlier work this paper cites.
A. Makadia, A. Patterson, and K. Daniilidis, “Fully automatic registration of 3d point clouds,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 1. IEEE, 2006, pp. 1297–1304
2006
Earlier work this paper cites.
M. Bosse and R. Zlot, “Continuous 3D Scan-Matching with a Spinning 2D Laser,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 4312–4319
2009
Earlier work this paper cites.
A. Segal, D. Haehnel, and S. Thrun, “Generalized-icp.” in Robotics: science and systems , vol. 2, no. 4. Seattle, WA, 2009, p. 435
2009
Earlier work this paper cites.
M. Bosse, R. Zlot, and P. Flick, “Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping,” IEEE Transactions on Robotics , vol. 28, no. 5, pp. 1104–1119, 2012
2012
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.
S. Lovegrove, A. Patron-Perez, and G. Sibley, “Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras,” in BMVC , vol. 2, no. 5, 2013, p. 8
2013
Earlier work this paper cites.
C. H. Tong, P. Furgale, and T. D. Barfoot, “Gaussian Process Gauss–Newton for non-parametric simultaneous localization and mapping,” The International Journal of Robotics Research , vol. 32, no. 5, pp. 507–525, 2013
2013
Earlier work this paper cites.
J. Zhang and S. Singh, “LOAM: Lidar Odometry and Mapping in Real-time,” in Proceedings of Robotics: Science and Systems , Berkeley, USA, July 2014
2014
Earlier work this paper cites.
H. Alismail, L. D. Baker, and B. Browning, “Continuous Trajectory Estimation for 3D SLAM from Actuated Lidar,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 6096–6101
2014
Earlier work this paper cites.
C. H. Tong, S. Anderson, H. Dong, and T. D. Barfoot, “Pose Interpolation for Laser-based Visual Odometry,” Journal of Field Robotics , vol. 31, no. 5, pp. 731–757, 2014
2014
Cited alongside, same era.
A. Patron-Perez, S. Lovegrove, and G. Sibley, “A Spline-Based Trajectory Representation for Sensor Fusion and Rolling Shutter Cameras,” International Journal of Computer Vision , vol. 113, no. 3, pp. 208–219, 2015
2015
Cited alongside, same era.
——, “Low-drift and real-time lidar odometry and Mapping,” Autonomous Robots , vol. 41, pp. 401–416, 2017
2017
Cited alongside, same era.
M. Grupp, “evo: Python package for the evaluation of odometry and slam.” https://github.com/MichaelGrupp/evo , 2017
2017
Cited alongside, same era.
J. Behley and C. Stachniss, “Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments,” in Robotics: Science and Systems , vol. 2018, 2018
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
Later among the works it cites.
G. Kim, Y.-S. Park, Y. Cho, J. Jeong, and A. Kim, “MulRan: Multimodal Range Dataset for Urban Place Recognition,” 2020 IEEE International Conference on Robotics and Automation (ICRA) , pp. 6246–6253, 2020
2020
Later among the works it cites.
C. Qin, H. Ye, C. E. Pranata, J. Han, S. Zhang, and M. Liu, “Lins: A lidar-inertial state estimator for robust and efficient navigation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 8899–8906
2020
Later among the works it cites.
W. Xu and F. Zhang, “FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3317–3324, 2021
2021
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2018
Cited alongside, same era.
C. Park, P. Moghadam, S. Kim, A. Elfes, C. Fookes, and S. Sridharan, “Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1206–1213
2018
Cited alongside, same era.
T. Shan and B. Englot, “LeGO-LOAM: Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 4758–4765
2018
Cited alongside, same era.
——, “Scan Context: Egocentric Spatial Descriptor for Place Recognition within 3D Point Cloud Map,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , Madrid, Oct. 2018
2018
Cited alongside, same era.
D. Droeschel and S. Behnke, “Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 5000–5007
2018
Cited alongside, same era.
H. Ye, Y. Chen, and M. Liu, “Tightly coupled 3d lidar inertial odometry and mapping,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3144–3150
2019
Cited alongside, same era.
C. Le Gentil, T. Vidal-Calleja, and S. Huang, “IN2LAMA: INertial Lidar Localisation and MApping,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 6388–6394
2019
Cited alongside, same era.
Later among the works it cites.
H. Wang, C. Wang, C.-L. Chen, and L. Xie, “F-LOAM: Fast LiDAR Odometry and Mapping,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 4390–4396
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Hudson, F. Talbot, M. Cox, J. Williams, T. Hines, A. Pitt, B. Wood, D. Frousheger, K. Lo Surdo, T. Molnar, R. Steindl, M. Wildie, I. Sa, N. Kottege, K. Stepanas, E. Hernandez, G. Catt, W. Docherty, B. Tidd, B. Tam, S. Murrell, M. Bessell, L. Hanson, L. Tychsen-Smith, H. Suzuki, L. Overs et al. , “Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61’s Approach to the DARPA Subterranean Challenge,” Field Robotics , vol. 2, pp. 557–594, 2022
2022
Closest in time.
W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar-inertial odometry,” IEEE Transactions on Robotics , pp. 1–21, 2022
2022
Closest in time.
C. Park, P. Moghadam, J. L. Williams, S. Kim, S. Sridharan, and C. Fookes, “Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM,” IEEE Transactions on Robotics , vol. 38, no. 2, pp. 978–997, 2022
2022
Closest in time.
“Automap,” https://automap.io , accessed: 2022-02-22
2022
Closest in time.
K. Vidanapathirana, M. Ramezani, P. Moghadam, S. Sridharan, and C. Fookes, “LoGG3D-Net: Locally guided global descriptor learning for 3D place recognition,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022
2022
Closest in time.