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Development of a Method for Data Dimensionality Reduction in Loop Closure Detection: An Incremental Approach
- Source :
- Robotica. 39:557-571
- Publication Year :
- 2020
- Publisher :
- Cambridge University Press (CUP), 2020.
-
Abstract
- SUMMARYThis article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.
- Subjects :
- 0209 industrial biotechnology
Computer science
General Mathematics
Dimensionality reduction
Coordinate system
Mobile robot
02 engineering and technology
Computer Science Applications
020901 industrial engineering & automation
Dimension (vector space)
Control and Systems Engineering
Sliding window protocol
0202 electrical engineering, electronic engineering, information engineering
Batch processing
020201 artificial intelligence & image processing
Representation (mathematics)
Algorithm
Software
Eigenvalues and eigenvectors
Subjects
Details
- ISSN :
- 14698668 and 02635747
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Robotica
- Accession number :
- edsair.doi...........30ae74008456648655ffd799605b7814