Robot navigation based on view sequences stored in a sparse distributed memory

Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.

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