Mismatched image identification using histogram of loop closure error for feature-based optical mapping

Image registration is one of the most fundamental steps in optical mapping from mobile platforms. Lately, image registration is performed by detecting salient points in two images and matching their descriptors. Robust methods [such as Random Sample Consensus (RANSAC)] are employed to eliminate outl...

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Bibliographic Details
Published in:International journal of intelligent robotics and applications Online 2019-06, Vol.3 (2), p.196-206
Main Authors: Elibol, Armagan, Chong, Nak-Young, Shim, Hyunjung, Kim, Jinwhan, Gracias, Nuno, Garcia, Rafael
Format: Article
Language:English
Subjects:
Artificial Intelligence
Closed loops
Computer Science
Control
Electronics and Microelectronics
Errors
Geometric transformation
Histograms
Image registration
Instrumentation
Machines
Manufacturing
Mapping
Mechatronics
Methods
Outliers (statistics)
Processes
Registration
Regular Paper
Robotics
User Interfaces and Human Computer Interaction
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Summary:Image registration is one of the most fundamental steps in optical mapping from mobile platforms. Lately, image registration is performed by detecting salient points in two images and matching their descriptors. Robust methods [such as Random Sample Consensus (RANSAC)] are employed to eliminate outliers and compute the geometric transformation between the coordinate frames of images, typically a homography when the images contain views of a flat area. However, the image registration pipeline can sometimes provide a sufficient number of wrong inliers within the error bounds even when images do not overlap at all. Such mismatches occur especially when the scene has repetitive texture and shows structural similarity. Such pairs prevent the trajectory (thus, a mosaic) from being estimated accurately. In this paper, we propose to utilize closed-loop constraints for identifying mismatches. Cycles appear when the camera revisits an area that was imaged before, which is a common practice especially for mapping purposes. The proposed method exploits the fact that images forming a cycle should have an identity mapping when all the homographies between images in the cycle are multiplied. Our proposal obtains error statistics for each matched image pair extracting several cycle bases. Then, by using a previously trained classifier, it identifies image pairs by comparing error histograms. We present experimental results with different image sequences.
ISSN:2366-5971
2366-598X
DOI:10.1007/s41315-019-00089-0