Pyramidal 3D feature fusion on polar grids for fast and robust traversability analysis on CPU

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 a pyramid-polar space representation based on SVM classifiers. In particular, we show that by fusing geometric features with information stemming from coarser pyramid levels that account for a broader space portion, as well as integrating important implementation details, allows for a noticeable boost in performance and reliability. The main goal of this work is to demonstrate that traversability analysis is possible with effective results and in real-time even on cheaper hardware than expensive GPUs, e.g. CPU-only PCs. The proposed approach has been compared with state-of-the-art deep learning approaches on publicly available datasets of outdoor driving scenarios, running such alg...