MiNERVA: Toposemantic Navigation Model based on Visual Information for Indoor Enviroments

Despite the great advances in robotics made in recent years, there is not yet a universal model that allows a robot to move independently, safely and taking into account the most important elements of the surrounding environment. This paper describes the theoretical implementation of MiNERVA. MiNERVA is a model for robot navigation in human indoor environments based on visual information, conceived to enable a robot to move autonomously with full-knowledge of the environment to enhance human-robot and robot-environment integration. When robots have to share environments with humans, perception and modelling gain importance. MiNERVA manages the information of the environment, the robot internal information and the robot-environment relation in order to understand, perceive and move intelligently and autonomously. Integration of the information and management of the uncertainty are key aspects for robot navigation. Results obtained with MiNERVA are presented to validate the approach.