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The treatment of image data for robotic applications such as navigation, path planning and localization has always been problematic when working in image space (using the appearance of the environment) rather than in Cartesian space (using the geometry of the environment). This is due to both computational overhead introduced by the large amount of data that needs to be manipulated and the high-dimensionality of the image space. We present results from an approach using an artificial immune network construction algorithm which dramatically reduces the dimensionality of the image space and generates network structures useful for navigation and localization. The technique uses the artificial immune network mechanism to link images with similar properties, thus corresponding to similar poses of the robot, into a network which can be displayed in two dimensions. This generates an intuitive representation of the environment which the robot has experienced in a way which can also be traversed in order to perform path-planning in the space of visual experiences. A network generated as a mobile robot moves around in its environment is presented, and related topologically to the movements made by the robot. Properties of the networks produced are discussed with relation to the visual complexity of the environment experienced by the robot. In general, regions of the environment which appear homogeneous produce fewer nodes and edges in the network, and regions of a more heterogeneous appearance produce denser, more highly connected network structures.