The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Online global loop closure detection for large-scale multi-session graph-based SLAM
Open accessAbstract
For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Human Memory: A Proposed System and its Control Processes
Atkinson, R. C., & Shiffrin, R. M. (1968) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60422-3 · Cited by 7,216
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Globally Consistent Range Scan Alignment for Environment Mapping
Lu, F., & Milios, E. (1997) · Autonomous Robots · doi:10.1023/a:1008854305733 · Cited by 1,304
Bosse, M., Newman, P., Leonard, J., & Teller, S. (2004) · The International Journal of Robotics Research · doi:10.1177/0278364904049393 · Cited by 305
Towards a robust back-end for pose graph SLAM
Sunderhauf, N., & Protzel, P. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224709 · Cited by 198
Loop closure detection in SLAM by combining visual and spatial appearance
Ho, K. L., & Newman, P. (2006) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2006.04.016 · Cited by 93
Cited by
Visual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079
Labbé, M., & Michaud, F. (2019) · Journal of Field Robotics · doi:10.1002/rob.21831 · Cited by 989
Self-citationA Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles
Zou, Q., Sun, Q., Chen, L., Nie, B., & Li, Q. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3063477 · Cited by 321
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175
RGB-D camera based wearable navigation system for the visually impaired
Lee, Y. H., & Medioni, G. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.03.019 · Cited by 143
Metadata
- Authors
- Labbe, M., & Michaud, F.
- Venue
- 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2014-09
- Pages
- 2661-2666
- OpenAlex
- W1968034339
- Cited by
- 373
- References
- 6
- FWCI
- 61.90
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Online global loop closure detection for large-scale multi-session graph-based SLAM. https://app.cochat.ai/papers/10.1109/iros.2014.6942926
- The paper
- Labbe, M., & Michaud, F. (2014). Online global loop closure detection for large-scale multi-session graph-based SLAM. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2014.6942926
Data sources
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.
