Abstract
Ant Colony Optimization (ACO) algorithms work by leveraging a population of agents that communicate through interaction with deposited “pheromone,” and have been applied in various configurations to the long-standing problem of identifying trafficable terrain from aerial imagery. While these approaches have proven successful in highlighting paved roads in urban, highly-developed sites, they tend to fail in peri-urban and rural locations due to the lower frequency of unnatural features. In this work, we describe a workflow that uses site-specific, near-infrared and first-return LIDAR data to predict the “accessible space” of an image–i.e., the more open regions with shallow elevation gradient that may be readily traversible by both mounted (e.g., all-terrain vehicles) and dismounted forces. Collectively, these regions are supplied as input to an ACO algorithm, modified so that the agents perform a random walk weighted by local elevation change, which allows for a more comprehensive exploration of increasingly featureless imaged terrain. Performance of this workflow is evaluated using two study sites in the continental United States: the Muscatatuck Urban Training Center in rural Indiana, and Camp Shelby in Mississippi. Comparison of results with ground-truth datasets show a high degree of success in predicting areas trafficable by a wide variety of mobile units.
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Rowland, M.A., Suir, G.M., Mayo, M.L., Davis, A. (2019). Swarm Optimization Algorithm for Road Bypass Extrapolation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_45
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DOI: https://doi.org/10.1007/978-3-030-33720-9_45
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