The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval.

Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost.

Special Issue on Visual Sensors.

Visual sensors have characteristics that make them interesting as sources of information for any process or system [...

Visual Information Fusion through Bayesian Inference for Adaptive Probability-Oriented Feature Matching.

This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance.

Estimating the position and orientation of a mobile robot with respect to a trajectory using omnidirectional imaging and global appearance.

Along the past years, mobile robots have proliferated both in domestic and in industrial environments to solve some tasks such as cleaning, assistance, or material transportation. One of their advantages is the ability to operate in wide areas without the necessity of introducing changes into the existing infrastructure. Thanks to the sensors they may be equipped with and their processing systems, mobile robots constitute a versatile alternative to solve a wide range of applications.

Position estimation and local mapping using omnidirectional images and global appearance descriptors.

This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform.

Performance of global-appearance descriptors in map building and localization using omnidirectional vision.

Map building and localization are two crucial abilities that autonomous robots must develop. Vision sensors have become a widespread option to solve these problems. When using this kind of sensors, the robot must extract the necessary information from the scenes to build a representation of the environment where it has to move and to estimate its position and orientation with robustness.

Estimation of visual maps with a robot network equipped with vision sensors.

In this paper we present an approach to the Simultaneous Localization and Mapping (SLAM) problem using a team of autonomous vehicles equipped with vision sensors. The SLAM problem considers the case in which a mobile robot is equipped with a particular sensor, moves along the environment, obtains measurements with its sensors and uses them to construct a model of the space where it evolves. In this paper we focus on the case where several robots, each equipped with its own sensor, are distributed in a network and view the space from different vantage points.

Map building and monte carlo localization using global appearance of omnidirectional images.

In this paper we deal with the problem of map building and localization of a mobile robot in an environment using the information provided by an omnidirectional vision sensor that is mounted on the robot. Our main objective consists of studying the feasibility of the techniques based in the global appearance of a set of omnidirectional images captured by this vision sensor to solve this problem. First, we study how to describe globally the visual information so that it represents correctly locations and the geometrical relationships between these locations.