Visual loop closure detection plays a key role in navigation systems for intelligent vehicles. Nowadays, state-of-the-art algorithms are focused on unidirectional loop closures, but there are situations where they are not sufficient for identifying previously visited places. Therefore, the detection of bidirectional loop closures when a place is revisited in a different direction provides a more robust visual navigation. We propose a novel approach for identifying bidirectional loop closures on panoramic image sequences. Our proposal combines global binary descriptors and a matching strategy based on cross-correlation of sub-panoramas, which are defined as the different parts of a panorama. A set of experiments considering several binary descriptors (ORB, BRISK, FREAK, LDB) is provided, where LDB excels as the most suitable. The proposed matching proffers a reliable bidirectional loop closure detection, which is not efficiently solved in any other previous research. Our method is successfully validated and compared against FAB-MAP and BRIEF-Gist. The Ford Campus and the Oxford New College datasets are considered for evaluation.

Astronomical images taken from large ground-based telescopes requires techniques as Adaptive Optics in order to improve their spatial resolution. In this work are presented computational results from a modified curvature sensor, the Tomographic Pupil Image Wavefront Sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional Neural Networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of Artificial Neural Networks (ANN), which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (Root Mean Square error, Mean Structural Similarity and Strehl ratio).In general, CNN trained as reconstructor showed slightly better performance than the conventional reconstruction in TPI-WFS for most of the turbulent profiles, but it made significant improvements for higher turbulent profiles that have the lowest r0 values.