Terrain Classification and Navigability Analysis in Unstructured Outdoor Environments
Terrain Classification and Navigability Analysis in Unstructured Outdoor Environments
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Date
2021
Authors
Guan, Tianrui
Advisor
Lin, Ming C
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Abstract
We present a new learning-based method for identifying safe and navigable regions inoff-road terrains and unstructured environments from RGB images. Our approach consists of
classifying groups of terrains based on their navigability levels using coarse-grained semantic
segmentation. We propose a transformer-based deep neural network architecture that uses a
novel group-wise attention mechanism to distinguish between navigability levels of different
terrains. Our group-wise attention heads enable the network to explicitly focus on the different
groups and improve the accuracy. We show through extensive evaluations on the RUGD and
RELLIS-3D datasets that our learning algorithm improves visual perception accuracy in off-road
terrains for navigation. We compare our approach with prior work on these datasets and achieve
an improvement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and 3.82-10.64% on
RELLIS-3D. In addition, we deploy our method on a Clearpath Jackal robot. Our approach
improves the performance of the navigation algorithm in terms of average progress towards the
goal by 54.73% and the false positives in terms of forbidden region by 29.96%.