Abstract
This paper examines the generation of visibility maps for the purposes of robotic path planning. The paper proposes a map construction technique that extends a version of the viewshed problem to calculate the rays to and from all discrete points on the map efficiently. The introduced form is parallelizable, making it suitable for fast GPU computation necessary for realtime calculation during path planning. New algorithms are introduced to generate worst- and best-case visibility maps by bounding precision uncertainties in the discretized map. Visibility theory is then applied to quantify the detectability of each viewpoint in the space based on atmospheric visibility models. Path planning is executed on the generated cost map illustrating its usability for visibility path planning.
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