The DOI matches its Crossref record.
Using occupancy grids for mobile robot perception and navigation
Abstract
An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered.>
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
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
A system for volumetric robotic mapping of abandoned mines
Thrun, S., Hahnel, D., Ferguson, D., Montemerlo, M., Triebel, R., Burgard, W., Baker, C., Omohundro, Z., Thayer, S., & Whittaker, W. (2004) · 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) · doi:10.1109/robot.2003.1242260 · Cited by 253
Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments
Wolf, D. F., & Sukhatme, G. S. (2005) · Autonomous Robots · doi:10.1007/s10514-005-0606-4 · Cited by 248
MP3: A Unified Model to Map, Perceive, Predict and Plan
Casas, S., Sadat, A., & Urtasun, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01417 · Cited by 228
Neural Topological SLAM for Visual Navigation
Singh Chaplot, D., Salakhutdinov, R., Gupta, A., & Gupta, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01289 · Cited by 241
Metadata
- Authors
- Elfes, A.
- Venue
- Computer
- Published
- 1989-06
- Volume
- 22
- Issue
- 6
- Pages
- 46-57
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W1999050017
- Cited by
- 2,213
- References
- 0
- FWCI
- 30.30
- 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: Using occupancy grids for mobile robot perception and navigation. https://app.cochat.ai/papers/10.1109/2.30720
- The paper
- Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer. https://doi.org/10.1109/2.30720
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.
