Modeling occluded areas in dynamic grid maps

Abstract

The dynamic grid map illustrates the environment of robots with moving and static obstacles. Nuss et al. describe in [1] an implementation of this grid map, in which the state of the grid cells is to be modeled as a random finite set (RFS) based on a stochastic measurement system. For a real-time implementation this approach was approximated with Dempster-Shafer (DS). For this Nuss et al. design the areas without information (unknown areas) so, that no probabilistic calculations are executed. Only in the field of view, hypotheses represent the dynamic behavior of objects. This hypotheses are generated with particles. Therefore, in [1] it was proposed to extend this modeling. In this paper a pure Bayes approach is presented, which calculates all areas of the dynamic grid map probabilistic. Now, the resulting modeling generates hypotheses, which represent the dynamic behavior of unobservable objects. Thus, objects moving out of unknown areas can be detected more quickly. This leads to a more intuitive understanding as well as representation of the environment.