The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map
Abstract
Compared to diverse feature detectors and descriptors used for visual scenes, describing a place using structural information is relatively less reported. Recent advances in simultaneous localization and mapping (SLAM) provides dense 3D maps of the environment and the localization is proposed by diverse sensors. Toward the global localization based on the structural information, we propose Scan Context, a non-histogram-based global descriptor from 3D Light Detection and Ranging (LiDAR) scans. Unlike previously reported methods, the proposed approach directly records a 3D structure of a visible space from a sensor and does not rely on a histogram or on prior training. In addition, this approach proposes the use of a similarity score to calculate the distance between two scan contexts and also a two-phase search algorithm to efficiently detect a loop. Scan context and its search algorithm make loop-detection invariant to LiDAR viewpoint changes so that loops can be detected in places such as reverse revisit and corner. Scan context performance has been evaluated via various benchmark datasets of 3D LiDAR scans, and the proposed method shows a sufficiently improved performance.
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Cites
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
Shape 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
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472
University of Michigan North Campus long-term vision and lidar dataset
Carlevaris-Bianco, N., Ushani, A. K., & Eustice, R. M. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915614638 · Cited by 576
Robust loop closing over time for pose graph SLAM
Latif, Y., Cadena, C., & Neira, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913498910 · Cited by 221
Cited by
Kim, G., Choi, S., & Kim, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3116424 · Cited by 319
Self-citationIntensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Wang, H., Wang, C., & Xie, L. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9196764 · Cited by 308
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
Lim, H., Hwang, S., & Myung, H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3061363 · Cited by 268
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Greve, E., Büchner, M., Vödisch, N., Burgard, W., & Valada, A. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610112 · Cited by 32
Metadata
- Authors
- Kim, G., & Kim, A.
- Venue
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 4802-4809
- OpenAlex
- W2910489334
- Cited by
- 904
- References
- 6
- FWCI
- 48.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kim, G., & Kim, A. (2018). Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2018.8593953
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