Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance