The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
AUV Navigation and Localization: A Review
Abstract
Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.
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
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
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
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
Shchepetkin, A. F., & McWilliams, J. C. (2005) · Ocean Modelling · doi:10.1016/j.ocemod.2004.08.002 · Cited by 5,145
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Simultaneous localization and mapping (SLAM): part II
Bailey, T., & Durrant-Whyte, H. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1678144 · Cited by 2,554
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
Grisetti, G., Stachniss, C., & Burgard, W. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.889486 · Cited by 2,439
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2002) · doi:10.5555/777092.777184 · Cited by 2,012
Cited by
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Multiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324
Self-citationGonzález-García, J., Gómez-Espinosa, A., Cuan-Urquizo, E., García-Valdovinos, L. G., Salgado-Jiménez, T., & Cabello, J. A. E. (2020) · Applied Sciences · doi:10.3390/app10041256 · Cited by 283
Communication-constrained multi-AUV cooperative SLAM
Paull, L., Huang, G., Seto, M., & Leonard, J. J. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139227 · Cited by 116
Self-citationDigital Twin: Values, Challenges and Enablers
Rasheed, A., San, O., & Kvamsdal, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01719 · Cited by 81
Metadata
- Authors
- Paull, L., Saeedi, S., Seto, M., & Li, H.
- Venue
- IEEE Journal of Oceanic Engineering
- Published
- 2014-01
- Volume
- 39
- Issue
- 1
- Pages
- 131-149
- ISSN
- 0364-9059 (ISSN-L)
- 1558-1691
- OpenAlex
- W1969142081
- Cited by
- 1,536
- References
- 86
- FWCI
- 104.20
- Type
- journal article
- Language
- en
- Open access
- closed
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: AUV Navigation and Localization: A Review. https://app.cochat.ai/papers/10.1109/joe.2013.2278891
- The paper
- Paull, L., Saeedi, S., Seto, M., & Li, H. (2014). AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering. https://doi.org/10.1109/joe.2013.2278891
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.
