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Traditional approaches to mapping of environments in robotics make use ofspatially discretized representations, such as occupancy grid maps. Modernsystems, e.g. in agriculture or automotive applications, are equipped with avariety of different sensors to gather diverse process-relevant modalities fromthe environment. The amount of data and its associated semantic informationdemand for broader data structures and frameworks, like semantical occupancygrid maps (SOGMs). This multi-modal representation also calls for novel methodsof path planning. Due to the sequential nature of path planning as aconsecutive execution of tasks and their ability to handle multi-modal data asprovided by SOGMs, Markovian models, such as Hidden Markov Models (HMM) orPartially Observable Markov Decision Processes, are applicable. Furthermore,for these techniques to be applied effectively and efficiently, data from SOGMsmust be extracted and refined. Superpixel algorithms, originating from computervision, provide a method to de-noise and re-express SOGMs in an alternativerepresentation. This publication explores and extends the use of superpixelsegmentation as a post-processing step and applies Markovian models for pathdecoding on SOGMs.

Path Evaluation via HMM on Semantical Occupancy Grid Maps