Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed.
Construction of Fuzzy Map for Autonomous Mobile Robots Based on Fuzzy Confidence Model
