The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
SegMatch: Segment based place recognition in 3D point clouds
Open accessAbstract
Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of `perfect segmentation', or on the existence of `objects' in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available.
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3D is here: Point Cloud Library (PCL)
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A 3-dimensional sift descriptor and its application to action recognition
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Visual Place Recognition: A Survey
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On the segmentation of 3D LIDAR point clouds
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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
Labbé, M., & Michaud, F. (2019) · Journal of Field Robotics · doi:10.1002/rob.21831 · Cited by 989
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Liu, Z., Zhou, S., Suo, C., Yin, P., Chen, W., Wang, H., Li, H., & Liu, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00292 · Cited by 325
Kim, G., Choi, S., & Kim, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3116424 · Cited by 319
Metadata
- Authors
- Dube, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., & Cadena, C.
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 5266-5272
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2527172881
- Cited by
- 339
- References
- 26
- FWCI
- 51.42
- Type
- conference paper
- Language
- en
- Open access
- green
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- Dube, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., & Cadena, C. (2017). SegMatch: Segment based place recognition in 3D point clouds. 2017 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2017.7989618
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
