SUMMARY
This 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.
This paper describes loop closures detection, a significant problem in mobile robotics, using analysis of similarity between images in a low-dimensional mapping. We represent a set of images as a graph in high-dimensional space, where each node is represented by a dominant eigenvector of the correspondent image. To this graph, we apply Diffusion Maps by Coifman and Lafon [4], a graph-based spectral method to data dimensionality reduction. We determine visual similarity analysis and detect loop closure in lower dimension, without building a vocabulary of visual words. Our experiments show results of loop closures detection both in indoor and outdoor environments from images captured by an RGB camera as well as images captured using Google Street View.
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