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Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art
- Source :
- IEEE Access, Vol 8, Pp 130561-130575 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described.
- Subjects :
- General Computer Science
genetic structures
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
ComputerApplications_COMPUTERSINOTHERSYSTEMS
GeneralLiterature_MISCELLANEOUS
Human–computer interaction
General Materials Science
binocular vision
Rescue robot
business.industry
Perspective (graphical)
General Engineering
Coal mining
technology, industry, and agriculture
Coal mine rescue robots
eye diseases
Stereopsis
Obstacle
Robot
stereo vision matching
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
camera calibration
Binocular vision
human activities
lcsh:TK1-9971
Camera resectioning
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....616dc22dd6a0fc31c19dfb081509cf2a