The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Low-drift and real-time lidar odometry and mapping
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Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“From feature's perspective, Barfoot et al.'s methods (Dong and Barfoot 2012; Anderson and Barfoot 2013a, b; Tong and Barfoot 2013) create visual images from laser intensity returns and match visually distinct features (Bay et al. 2008) between images to recover motion.”
BackgroundVision 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
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
3D is here: Point Cloud Library (PCL)
Rusu, R. B., & Cousins, S. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 4,885
Efficient variants of the ICP algorithm
Rusinkiewicz, S., & Levoy, M. (2002) · Proceedings Third International Conference on 3-D Digital Imaging and Modeling · doi:10.1109/im.2001.924423 · Cited by 3,945 · cited 1×
“The two point clouds are matched and compared using the point to plane ICPmethod (Rusinkiewicz and Levoy 2001).”
MethodLOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Self-citation
Cited by
Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain
Shan, T., & Englot, B. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594299 · Cited by 2,295
Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping
Shan, T., Englot, B., Ratti, C., & Rus, D. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561996 · Cited by 624
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Dellenbach, P., Deschaud, J.-E., Jacquet, B., & Goulette, F. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811849 · Cited by 280
Recent advancements of robotics in construction
Xiao, B., Chen, C., & Yin, X. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104591 · Cited by 204
Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality
Lee, J., Balachandran, R., Kondak, K., Coelho, A., Stefano, M. D., Humt, M., Feng, J., Asfour, T., & Triebel, R. (2024) · IEEE Transactions on Field Robotics · doi:10.55417/fr.2023010 · Cited by 8
Metadata
- Authors
- Zhang, J., & Singh, S.
- Venue
- Autonomous Robots
- Published
- 2016-02-18
- Volume
- 41
- Issue
- 2
- Pages
- 401-416
- ISSN
- 0929-5593 (ISSN-L)
- 1573-7527
- OpenAlex
- W2277848489
- Cited by
- 820
- References
- 36
- FWCI
- 40.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, J., & Singh, S. (2017). Low-drift and real-time lidar odometry and mapping. Autonomous Robots. https://doi.org/10.1007/s10514-016-9548-2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
