  Traditional approaches to mapping of environments in robotics make use of
spatially discretized representations, such as occupancy grid maps. Modern
systems, e.g. in agriculture or automotive applications, are equipped with a
variety of different sensors to gather diverse process-relevant modalities from
the environment. The amount of data and its associated semantic information
demand for broader data structures and frameworks, like semantical occupancy
grid maps (SOGMs). This multi-modal representation also calls for novel methods
of path planning. Due to the sequential nature of path planning as a
consecutive execution of tasks and their ability to handle multi-modal data as
provided by SOGMs, Markovian models, such as Hidden Markov Models (HMM) or
Partially Observable Markov Decision Processes, are applicable. Furthermore,
for these techniques to be applied effectively and efficiently, data from SOGMs
must be extracted and refined. Superpixel algorithms, originating from computer
vision, provide a method to de-noise and re-express SOGMs in an alternative
representation. This publication explores and extends the use of superpixel
segmentation as a post-processing step and applies Markovian models for path
decoding on SOGMs.
