Pushing the Limits of Learning-Based Traversability Analysis for Autonomous Driving on CPU

Self-driving vehicles and autonomous ground robots require a reliable accurate method to analyze the traversability of surrounding environment for safe navigation. This paper proposes evaluates real-time machine learning-based Traversability Analysis that combines geometric features with appearance-based in hybrid approach based on SVM classifier. In particular, we show integrating new set visual focusing important implementation details enables noticeable boost performance reliability. The proposed has been compared state-of-the-art Deep Learning approaches public dataset outdoor driving scenarios. It reaches an accuracy 89.2% scenarios varying complexity, demonstrating its effectiveness robustness. runs fully CPU comparable results respect other methods, operates faster, requires fewer hardware resources.