The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Simultaneous localization and mapping with unknown data association using FastSLAM
Abstract
The extended Kalman filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, real-world environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative approach based on the Rao-Blackwellized Particle Filter, has been shown to scale logarithmically with the number of landmarks in the map. This efficiency enables FastSLAM to be applied to environments far larger than could be handled by the EKF. In this paper, we show that FastSLAM also substantially outperforms the EKF in environments with ambiguous data association. The performance of the two algorithms is compared on a real-world data set with various levels of odometric noise. In addition, we show how negative information can be incorporated into FastSLAM in order to improve the accuracy of the estimated map.
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
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
A solution to the simultaneous localization and map building (SLAM) problem
Dissanayake, M. W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.938381 · Cited by 2,661
Cited by
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
Visual simultaneous localization and mapping: a survey
Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9365-8 · Cited by 883
Grisetti, G., Stachniss, C., & Burgard, W. (2005) · Proceedings of the 2005 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2005.1570477 · Cited by 734
Information-Based Compact Pose SLAM
Ila, V., Porta, J. M., & Andrade-Cetto, J. (2010) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2034435 · Cited by 183
Differential evolution solution to the SLAM problem
Moreno, L., Garrido, S., Blanco, D., & Muñoz, M. L. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.05.005 · Cited by 29
Metadata
- Authors
- Montemerlo, M., & Thrun, S.
- Venue
- 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)
- Publisher
- IEEE
- Published
- 2004-03-22
- Pages
- 1985-1991
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2115286699
- Cited by
- 462
- References
- 2
- FWCI
- 52.33
- Type
- conference paper
- 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: Simultaneous localization and mapping with unknown data association using FastSLAM. https://app.cochat.ai/papers/10.1109/robot.2003.1241885
- The paper
- Montemerlo, M., & Thrun, S. (2004). Simultaneous localization and mapping with unknown data association using FastSLAM. 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422). https://doi.org/10.1109/robot.2003.1241885
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.
