Autonomous vehicles often perform navigation and path planning using hierarchical control systems. These systems separate high and low level reasoning through an abstraction of the planning problem. For reasoning about terrain information, we present a method of abstraction that retains the finest level of resolution while progressing through greater levels of abstraction. Abstraction arises from a continuum of Gaussian smoothed terrain surfaces; each smoothed surface describes the terrain at a different scale of abstraction. We refer to this continuum as scale-space. For each level of abstraction, important features can be extracted from land elevation data for planning purposes. In this paper, we present this abstraction method, a graph representation for retaining scale-space information, and examples of how features from various levels of abstraction influence planning at different levels of a hierarchical control system.
"Planning With Abstraction: Map Data Feature Extraction In Scale-Space", Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969256; https://doi.org/10.1117/12.969256