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Semantic RGB-D SLAM for Rescue Robot Navigation
- Source :
- IEEE Access, Vol 8, Pp 221320-221329 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In this paper, we propose a semantic simultaneous localization and mapping (SLAM) framework for rescue robots, and report its use in navigation tasks. Our framework can generate not only geometric maps in the form of dense point-clouds but also corresponding point-wise semantic labels generated by a semantic segmentation convolutional neural network (CNN). The semantic segmentation CNN is trained using our RGB-D dataset of the RoboCup Rescue-Robot-League (RRL) competition environment. With the help of semantic information, the rescue robot can identify different types of terrains in a complex environment, so as to avoid specific obstacles or to choose routes with better traversability. To reduce the segmentation noise, our approach utilizes depth images to perform filtering on the segmentation results of each frame. The overall semantic map is then further improved in the point-cloud voxels. By accumulating results of multiple frames in the voxels, semantic maps with consistent semantic labels are obtained. To show the advantage of having a semantic map of the environment, we report a case study of how the semantic map can be utilized in a navigation task to reduce the arrival time while ensuring safety. The experimental result shows that our semantic SLAM framework is capable of generating a dense semantic map for the complex RRL competition environment, with which the arrival time of the navigation time is effectively reduced.
- Subjects :
- 0209 industrial biotechnology
General Computer Science
Computer science
02 engineering and technology
Simultaneous localization and mapping
Semantics
Convolutional neural network
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
rescue robot
Frame (artificial intelligence)
RoboCup
General Materials Science
Computer vision
Segmentation
path planning
Rescue robot
business.industry
General Engineering
Deep learning
Image segmentation
semantic SLAM
020201 artificial intelligence & image processing
Artificial intelligence
Noise (video)
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....60c48ef2d9d67d04029a7c3670e815d1