The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Open accessAbstract
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage on matching of local or global geometrical-only descriptors which discard intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. We propose a novel global descriptor, intensity scan context (ISC), that explores both geometry and intensity characteristics. To improve the efficiency for loop closure detection, an efficient two-stage hierarchical re-identification process is proposed, including binary-operation based fast geometric relation retrieval and intensity structure re-identification. Thorough experiments including both local experiment and public datasets test have been conducted to evaluate the performance of the proposed method. Our method achieves better recall rate and recall precision than existing geometric-only methods.
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Cites
Vision 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
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · cited 6×
“1) Temporal consistency check: In a SLAM system, it is observed that the occurrence of single loop closure often implies high similarity on the neighbour LiDAR scans since the sensor feedback is continuous in time [25].”
MethodInfluentialShape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
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
Fast Point Feature Histograms (FPFH) for 3D registration
Rusu, R. B., Blodow, N., & Beetz, M. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152473 · Cited by 3,878
Method for registration of 3-D shapes:
Besl, P. J., & McKay, N. D. (1992) · SPIE Proceedings · doi:10.1117/12.57955 · Cited by 2,413
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
Bags of Binary Words for Fast Place Recognition in Image Sequences
Galvez-López, D., & Tardos, J. D. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2197158 · Cited by 1,913
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
Milford, M. J., & Wyeth, G. F. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224623 · Cited by 981
Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map
Kim, G., & Kim, A. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593953 · Cited by 904
Cited by
Kim, G., Choi, S., & Kim, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3116424 · Cited by 319
Metadata
- Authors
- Wang, H., Wang, C., & Xie, L.
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 2095-2101
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3011465262
- Cited by
- 308
- References
- 27
- FWCI
- 46.05
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, H., Wang, C., & Xie, L. (2020). Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection. 2020 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra40945.2020.9196764
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