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TB-places
- Source :
- IEEE Access, Vol 7, Pp 52277-52287 (2019), IEEE Access, 7:8698240, 52277-52287. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Year :
- 2019
- Publisher :
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019.
-
Abstract
- Place recognition can be achieved by identifying whether a pair of images (a labeled reference image and a query image) depict the same place, regardless of appearance changes due to different viewpoints or lighting conditions. It is an important component of systems for camera localization and for loop closure detection and a widely studied problem for indoor or urban environments. Recently, the use of robots in agriculture and automatic gardening has created new challenges due to the highly repetitive appearance with prevalent green color and repetitive texture of garden-like scenes. The lack of available data recorded in gardens or plant fields makes difficult to improve localization algorithms for such environments. In this paper, we propose a new data set of garden images for testing algorithms for visual place recognition. It contains images with ground truth camera pose recorded in real gardens at different times, with varying light conditions. We also provide ground truth for all possible pairs of images, indicating whether they depict the same place or not. We also performed a thorough benchmark of several holistic (whole-image) descriptors, and provide the results on the proposed data set. We observed that existing descriptors have difficulties with scenes with repetitive textures and large changes of camera viewpoint.
- Subjects :
- General Computer Science
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
data set
02 engineering and technology
Benchmark
01 natural sciences
computer vision
Image (mathematics)
Component (UML)
holistic image descriptor
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Computer vision
visual place recognition
Ground truth
business.industry
020208 electrical & electronic engineering
010401 analytical chemistry
General Engineering
0104 chemical sciences
Data set
Closure (mathematics)
Benchmark (computing)
Robot
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....46a9c4945b3712efec5d4de4f114352c