Mapping with Sparse Local Sensors and Strong Hierarchical Priors

The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem in environments where only sparse, local sensory information is available. To compensate for these weak likelihoods, we make use of strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting and novelly deployed as a mapping algorithm. We give proof of concept results, intended to demonstrate the algorithm’s applicability as a part of a tactile SLAM module for the whiskered SCRATCHbot mobile robot platform.