The DOI matches its Crossref record.
Dempster-Shafer theory for sensor fusion in autonomous mobile robots
Abstract
This article discusses Dempster-Shafer (DS) theory in terms of its utility for sensor fusion for autonomous mobile robots. It exploits two little used components of DS theory: the weight of conflict metric and the enlargement of the frame of discernment. The weight of conflict is used to measure the amount of consensus between different sensors. A lack of consensus leads the robot to either compensate within certain limits or investigate the problem further, adding robustness to the robot's operation. Enlarging the frame of discernment allows a modular decomposition of evidence. This decomposition offers the advantages of perceptual abstraction, and permits expert knowledge about the domain to be embedded in the frames of discernment, simplifying the construction and maintenance of the knowledge base. Six experiments using this Dempster-Shafer framework are presented. Data from four types of sensor data were collected by a mobile robot and fused with the sensor fusion effects (SFX) architecture.
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
Cited by
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Approaches to Multisensor Data Fusion in Target Tracking: A Survey
Smith, D., & Singh, S. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.183 · Cited by 379
Multimodal representation learning for place recognition using deep Hebbian predictive coding
Pearson, M. J., Dora, S., Struckmeier, O., Knowles, T. C., Mitchinson, B., Tiwari, K., Kyrki, V., Bohte, S., & Pennartz, C. M. A. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.732023 · Cited by 19
Metadata
- Authors
- Murphy, R. R.
- Venue
- IEEE Transactions on Robotics and Automation
- Published
- 1998-04
- Volume
- 14
- Issue
- 2
- Pages
- 197-206
- ISSN
- 1042-296X (ISSN-L)
- OpenAlex
- W2153596970
- Cited by
- 236
- References
- 0
- FWCI
- 5.97
- 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: Dempster-Shafer theory for sensor fusion in autonomous mobile robots. https://app.cochat.ai/papers/10.1109/70.681240
- The paper
- Murphy, R. R. (1998). Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Transactions on Robotics and Automation. https://doi.org/10.1109/70.681240
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.
