The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Vision and navigation for the Carnegie-Mellon Navlab
Abstract
A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools. Results are described for vision and navigation tests using a mobile testbed that integrates perception and navigation capabilities that are based on two types of vision algorithms: color vision for road following, and 3-D vision for obstacle detection and avoidance. The perception modules are integrated into a system that allows the vehicle to drive continuously in an actual outdoor environment. The resulting system is able to navigate continuously on roads while avoiding obstacles.>
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Cites
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799
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Autonomous vehicle perception: The technology of today and tomorrow
Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.02.012 · Cited by 869
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Autonomous driving in urban environments: approaches, lessons and challenges
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Metadata
- Authors
- Thorpe, C., Hebert, M. H., Kanade, T., & Shafer, S. A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1988-05
- Volume
- 10
- Issue
- 3
- Pages
- 362-373
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2069239883
- Cited by
- 776
- References
- 1
- FWCI
- 99.23
- Type
- journal article
- Language
- en
- Open access
- closed
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- Thorpe, C., Hebert, M. H., Kanade, T., & Shafer, S. A. (1988). Vision and navigation for the Carnegie-Mellon Navlab. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.3900
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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.
