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Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU. (arXiv:2206.03083v1 [cs.RO])
cs.CV updates on arXiv.org arxiv.org
Self-driving vehicles and autonomous ground robots require a reliable and
accurate method to analyze the traversability of the surrounding environment
for safe navigation. This paper proposes and evaluates a real-time machine
learning-based Traversability Analysis method that combines geometric features
with appearance-based features in a hybrid approach based on a SVM classifier.
In particular, we show that integrating a new set of geometric and visual
features and focusing on important implementation details enables a noticeable
boost in performance and reliability. The …
analysis arxiv autonomous autonomous driving cpu driving learning