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 . We run loop-closure experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
In this letter , we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest . We propose a framework that uses these descriptors to detect previously seen observations and landmark correspondences, even with partial overlap and noise . We run loop closure detection experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
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polygons derived from Urquhart tessellations on
Urquhart tessellations derived from
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detected from lidar scans. We present
. We propose
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leverages these polygons to generate a signature that is used
uses these descriptors to
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and landmark correspondences,
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different levels of noisewhile also inferring landmark correspondences to compute an affine transformation between observations
noise
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loop-closure
loop closure detection