If this sounds familiar to you, it’s because this method works very similarly to error-diffusion. Instead of letting adjacent pixels compensate for the error, we’re letting each successive candidate compensate for the combined error of all previous candidates.
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It then uses the standard Dijkstra algorithm on the detailed local map within your start cluster to find the best paths from your actual start location to all border points of that starting cluster.