Nowadays, the design of fully autonomous mobile robots is a key discipline. Building a robust model of the unknown environment is an important ability the robot must develop. Using this model, this robot must be able to estimate its current position and to navigate to the target points. The use of omnidirectional vision sensors is usual to solve these tasks. When using this source of information, the robot must extract relevant information from the scenes both to build the model and to estimate its position. The possible frameworks include the classical approach of extracting and describing local features or working with the global appearance of the scenes, which has emerged as a conceptually simple and robust solution. While feature-based techniques have been extensively studied in the literature, appearance-based ones require a full comparative evaluation to reveal the performance of the existing methods and to tune correctly their parameters. This work carries out a comparative evaluation of four global-appearance techniques in map building tasks, using omnidirectional visual information as the only source of data from the environment.

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. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown) position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images) taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods.

Currently, many tasks can be carried out using mobile robots. These robots must be able to estimate their position in the environment to plan their actions correctly. Omnidirectional vision sensors constitute a robust choice to solve this problem, since they provide the robot with complete information from the environment where it moves. The use of global appearance or holistic methods along with omnidirectional images constitutes a robust approach to estimate the robot position when its movement is restricted to the ground plane. However, in some applications, the robot changes its altitude with respect to this plane, and this altitude must be estimated. This work focuses on this problem. A method based on the use of holistic descriptors is proposed to estimate the relative altitude of the robot when it moves upwards or downwards. This descriptor is constructed from the Radon transform of omnidirectional images captured by a catadioptric vision system. To estimate the altitude, the descriptor of the image captured from the current position is compared with the descriptor of the reference image, previously built. The framework is based on the use of phase correlation to calculate relative orientation and a method based on the compression-expansion of the columns of the holistic descriptor to estimate relative height. Only an omnidirectional vision sensor and image processing techniques are used to solve these problems. This approach has been tested using different sets of images captured both indoors and outdoors under realistic working conditions. The experimental results prove the validity of the method even in the presence of noise or occlusions.

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