Topological mapping using vision and a sparse distributed memory

Navigation based on visual memories is very common among humans. However, planning long trips requires a more sophisticated representation of the environment, such as a topological map, where connections between paths are easily noted. The present approach is a system that learns paths by storing sequences of images and image information in a sparse distributed memory (SDM). Connections between paths are detected by exploring similarities in the images, using the same SDM, and a topological representation of the paths is created. The robot is then able to plan paths and switch from one path to another at the connection points. The system was tested under reconstitutions of country and urban environments, and it was able to successfully map, plan paths and navigate autonomously. © 2011 Springer Science+Business Media B.V.