The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Dynamic Maps for Long-Term Operation of Mobile Service Robots
Open accessAbstract
This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stabilityplasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale.Robust statistics are used to interpret the samples.It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes.The method was evaluated in a five week experiment in a real dynamic environment.Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.
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
Globally Consistent Range Scan Alignment for Environment Mapping
Lu, F., & Milios, E. (1997) · Autonomous Robots · doi:10.1023/a:1008854305733 · Cited by 1,304
Cited by
AUV Navigation and Localization: A Review
Paull, L., Saeedi, S., Seto, M., & Li, H. (2014) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2013.2278891 · Cited by 1,536
iSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362
A Tutorial on Graph-Based SLAM
Grisetti, G., Kummerle, R., Stachniss, C., & Burgard, W. (2010) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2010.939925 · Cited by 1,345
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
iSAM: Incremental Smoothing and Mapping
Kaess, M., Ranganathan, A., & Dellaert, F. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2006706 · Cited by 1,076
Metadata
- Authors
- Biber, P., & Duckett, T.
- Venue
- Robotics: Science and Systems I
- Published
- 2005-06-08
- Volume
- 01
- Pages
- 17-24
- OpenAlex
- W3143919370
- Cited by
- 334
- References
- 1
- FWCI
- 47.28
- Type
- conference paper
- Language
- en
- Open access
- gold
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
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.
