Fetching the paper…
Reading the bibliography…
Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms.
Y. Chen and G. Medioni, “Object modelling by registration of multiple range images,” Image and Vision Computing , 1992
1992
Earlier work this paper cites.
A. Segal, D. Haehnel, and S. Thrun, “Generalized-icp.” in Robotics: Science and Systems (RSS) , 2009
2009
Earlier work this paper cites.
N. Bhatia, “Survey of nearest neighbor techniques,” International Journal of Computer Science and Information Security , 2010
2010
Earlier work this paper cites.
R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in IEEE International Conference on Robotics and Automation , 2011
2011
Earlier work this paper cites.
J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real-time.” in Robotics: Science and Systems , 2014
2014
Earlier work this paper cites.
J. L. Blanco and P. K. Rai, “nanoflann: a C++ header-only fork of FLANN, a library for nearest neighbor (NN) with kd-trees,” https://github.com/jlblancoc/nanoflann , 2014
2014
Earlier work this paper cites.
C. Cadena, L. Carlone et al. , “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics , 2016
2016
Earlier work this paper cites.
W. Hess, D. Kohler et al. , “Real-time loop closure in 2d lidar slam,” in IEEE International Conference on Robotics and Automation , 2016
2016
Cited alongside, same era.
T. Shan and B. Englot, “Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,” in International Conference on Intelligent Robots and Systems , 2018
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 , 2018
2018
Cited alongside, same era.
H. Ye, Y. Chen, and M. Liu, “Tightly coupled 3d lidar inertial odometry and mapping,” in International Conference on Robotics and Automation , 2019
2019
Cited alongside, same era.
K. Ebadi, Y. Chang et al. , “Lamp: Large-scale autonomous mapping and positioning for exploration of perceptually-degraded subterranean environments,” in IEEE International Conference on Robotics and Automation , 2020
M. Palieri, B. Morrell et al. , “Locus: A multi-sensor lidar-centric solution for high-precision odometry and 3d mapping in real-time,” IEEE Robotics and Automation Letters , 2020
2020
Later among the works it cites.
T. Shan, B. Englot et al. , “Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping,” in IEEE International Conference on Robotics and Automation , 2021
2021
Closest in time.
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 , 2021
2021
Closest in time.
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
T. Shan, B. Englot et al. , “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020
2020
Cited alongside, same era.
E. Nelson, “B(erkeley) l(ocalization) a(nd) m(apping).” [Online]. Available: https://github.com/erik-nelson/blam
Cited in the paper.
2021
Closest in time.
K. Koide, M. Yokozuka et al. , “Voxelized gicp for fast and accurate 3d point cloud registration,” in IEEE International Conference on Robotics and Automation , 2021
2021
Closest in time.