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.

翻译：自驾驶飞行器和自主地面机器人需要可靠和准确的方法来分析周围环境的可穿越性,以便安全航行。本文件建议并评价一种实时机器基于学习的易变性分析方法,该方法将几何特征与外观特征结合起来,采用基于SVM分类器的混合方法。特别是,我们表明,结合一套新的几何和视觉特征,并侧重于重要的执行细节,可以明显提高性能和可靠性。拟议方法已经与关于户外驾驶情景公共数据集的最先进的深学习方法进行了比较。在复杂程度不同的情况下,该方法的精确度达到89.2%,显示了其有效性和稳健性。该方法完全在CPU上运行,与其他方法取得可比的结果,运行速度更快,所需硬件资源更少。