Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU
| Autor: | Fusaro, Daniel, Olivastri, Emilio, Evangelista, Daniele, Imperoli, Marco, Menegatti, Emanuele, Pretto, Alberto |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the 17th International Conference on Intelligent Autonomous Systems (IAS 2022) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-031-22216-0_36 |
| Popis: | 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 proposed approach has been compared with state-of-the-art Deep Learning approaches on a public dataset of outdoor driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying complexity, demonstrating its effectiveness and robustness. The method runs fully on CPU and reaches comparable results with respect to the other methods, operates faster, and requires fewer hardware resources. Comment: Accepted to 17th International Conference on Intelligent Autonomous Systems (IAS-17) |
| Databáze: | arXiv |
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