Before revision: In this letter  we present a novel descriptor based on polygons derived from Urquhart tessellations on the position of trees in a forest detected from lidar scans. We present a framework that leverages these polygons to generate a signature that is used detect previously seen observations  even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .
After revision: In this letter we present a novel descriptor based on polygons derived from Urquhart tessellations on the position of trees in a forest . We propose a framework that leverages these polygons to generate a signature that is used detect previously seen observations even with partial overlap and different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations .