Semantical occupancy grid mapping framework

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In recent decades, mapping has been increasingly investigated and applied in unmanned terrain, aerial, sea, and underwater vehicles. While exploiting various mapping techniques to build an inner representation of the environment, one of the most famous remaining is occupancy grid mapping. It has been applied to all domains in a 2D/3D fashion for localization, mapping, navigation, and safe path traversal. Until now generally active range measuring sensors like LiDAR or SONAR are exploited to build those maps. With this work the authors want to overcome these barriers by presenting an occupancy mapping framework offering a generic sensor interface. The interface handles occupancy grids as inverse sensor models, which may represent knowledge on different semantical decision levels and therefore build up a semantic grid map stack. The framework offers buffered memory management for efficient storing and shifting and further services for accessing the 2D map stack in different cell-wise pre-fused and topometric ways. Within the framework, two novel techniques operating especially with occupancy grids are presented: First, a novel odds based interpolation filter is introduced, which scales grid maps in a Bayesian way. Second, a Supercell Extracted via Variance-Driven Sampling (SEVDS) algorithm is presented which, abstracts the semantical occupancy grid stack to a topometric map. While this work focuses on the framework's introduction, it is extended by the evaluation of SEVDS against state-of-the-art superpixel approaches to prove its applicability.