Morphological neural networks for vision based self-localization
Morphological neural networks (MNN) have been proposed as associative memories (with its two cases: autoassociative and heteroassociative). In this paper we are involved with heteroassociative MNN (HMNN). We propose their use for self-localization in a vision-based navigation framework for mobile robots. HMNN can be trained in a single computation step. Their storage capacity bound is the dimension of the patterns, and they have perfect recall of the patterns under very mild conditions. Recall is also very fast, because the MNN recall does not involve the search for an energy minimum.