Autonomous Navigation and Mapping using Monocular Low-Resolution Grayscale Vision

Vision has been a powerful tool for navigation of intelligent and man-made systems ever since the cybernetics revolution in the 1970s. There have been two basic approaches to the navigation of computer controlled systems: The self-contained bottom-up development of sensorimotor abilities, namely perception and mobility, and the top-down approach, namely artificial intelligence, reasoning and knowledge based methods. The three-fold goal of autonomous exploration, mapping and localization of a mobile robot however, needs to be developed within a single framework. An algorithm is proposed to answer the challenges of autonomous corridor navigation and mapping by a mobile robot equipped with a single forward-facing camera. Using a combination of corridor ceiling lights, visual homing, and entropy, the robot is able to perform straight line navigation down the center of an unknown corridor. Turning at the end of a corridor is accomplished using Jeffrey divergence and time-to-collision, while deflection from dead ends and blank walls uses a scalar entropy measure of the entire image. When combined, these metrics allow the robot to navigate in both textured and untextured environments. The robot can autonomously explore an unknown indoor environment, recovering from difficult situations like corners, blank walls, and initial heading toward a wall. While exploring, the algorithm constructs a Voronoi-based topo-geometric map with nodes representing distinctive places like doors, water fountains, and other corridors. Because the algorithm is based entirely upon low-resolution (32 x 24) grayscale images, processing occurs at over 1000 frames per second

Estudio técnico del estado del arte sobre el procesamiento digital de imágenes de 360 grados orientado a la navegación de vehículos aéreos no tripulados (drones).

El propósito de este documento es presentar un estudio técnico de publicaciones en las dos últimas décadas, sobre técnicas y algoritmos de procesamiento digital de imágenes de 360° orientadas a la navegación de Drones. Estas publicaciones se clasificaron según la metodología de Mapeo Sistemático (SMS). Para ello se han definido preguntas de investigación relacionadas con el tema en cuestión. Las bases de datos consultadas fueron: Scopus, ScienceDirect, IEEE, Google Scholar. Los resultados de este estudio muestran tendencias a 2 técnicas y 4 algoritmos que son los más utilizados. Para el caso de Algoritmos Filtro de Kalman, SIFT, SURF y redes neuronales convolucionales. Para técnicas SLAM y visión por sonar. Finalmente, se realiza una discusión de los mismos.The purpose of this document is the technical study of publications, the latest techniques, techniques and digital image processing algorithms of 360 ° oriented to the navigation of drone. These publications are classified according to the Systematic Mapping methodology (SMS). To this end, research questions related to the subject in question have been defined. The consulted databases were: Scopus, ScienceDirect, IEEE, Google Scholar. The results of this study show tendencies to 2 techniques and 4 algorithms. For the case of Kalman Filter Algorithms, SIFT, SURF and convolutional neural networks. For SLAM and sonar vision techniques. Finally, a discussion of them is made