The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
An evaluation of 2D SLAM techniques available in Robot Operating System
Open accessAbstract
In this work, a study of several laser-based 2D Simultaneous Localization and Mapping (SLAM) techniques available in Robot Operating System (ROS) is conducted. All the approaches have been evaluated and compared in 2D simulations and real world experiments. In order to draw conclusions on the performance of the tested techniques, the experimental results were collected under the same conditions and a generalized performance metric based on the k-nearest neighbors concept was applied. Moreover, the CPU load of each technique is examined. This work provides insight on the weaknesses and strengths of each solution. Such analysis is fundamental to decide which solution to adopt according to the properties of the intended final application.
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
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
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
G2o: A general framework for graph optimization
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., & Burgard, W. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5979949 · Cited by 1,982
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Globally Consistent Range Scan Alignment for Environment Mapping
Lu, F., & Milios, E. (1997) · Autonomous Robots · doi:10.1023/a:1008854305733 · Cited by 1,304
A flexible and scalable SLAM system with full 3D motion estimation
Kohlbrecher, S., von Stryk, O., Meyer, J., & Klingauf, U. (2011) · 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics · doi:10.1109/ssrr.2011.6106777 · Cited by 1,086
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
Thrun, S., & Montemerlo, M. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906065387 · Cited by 689
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
Huang, S., & Dissanayake, G. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.903811 · Cited by 402
Cited by
A 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
A study on the deployment of a service robot in an elderly care center
Portugal, D., Alvito, P., Christodoulou, E., Samaras, G., & Dias, J. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-018-0492-5 · Cited by 130
Self-citation
Metadata
- Authors
- Santos, J. M., Portugal, D., & Rocha, R. P.
- Venue
- 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
- Publisher
- IEEE
- Published
- 2013-10
- Pages
- 1-6
- OpenAlex
- W1999286188
- Cited by
- 254
- References
- 15
- FWCI
- 16.52
- 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: An evaluation of 2D SLAM techniques available in Robot Operating System. https://app.cochat.ai/papers/10.1109/ssrr.2013.6719348
- The paper
- Santos, J. M., Portugal, D., & Rocha, R. P. (2013). An evaluation of 2D SLAM techniques available in Robot Operating System. 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). https://doi.org/10.1109/ssrr.2013.6719348
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.
