Path Evaluation via HMM on Semantical Occupancy Grid Maps

TL;DR

This paper introduces a novel approach combining superpixel segmentation and Hidden Markov Models to improve path evaluation in semantical occupancy grid maps for robotics, accommodating multi-modal environmental data.

Contribution

It extends superpixel segmentation as a post-processing step and applies HMMs for path decoding on SOGMs, enhancing path planning with multi-modal data.

Findings

Superpixel segmentation effectively denoises SOGMs.

HMM-based path decoding improves planning accuracy.

Method adapts to diverse sensor modalities.

Abstract

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.