Abstract
Robots require environmental maps to move autonomously in the human living environment. Techniques for constructing environmental maps which have been proposed in the past mostly construct environmental maps based on shape information alone. However, when only shape information is used there is the problem that, in uniform environments which have few geometrical features, such as corridors, multiple candidates for the robot's own position are generated and its estimation is difficult. The present paper therefore focused on visual information as useful data to estimate the robot's own position, and investigated a self-location estimation method using only image information. For the proposed technique, learning was conducted using the SOM algorithm for unsupervised learning of omnidirectional image data, obtained in the environment in advance, and a self-location discriminator was built. The robot determines, by means of the location estimation discriminator, in which area in the environment the images it observed while moving could have been taken. In this article we have verified the usability of the location estimation discriminator for an indoor environment with many geometrical features and rich in visual information such as patterns and textures, and for a corridor environment with few geometrical features and poor in visual information.
