Autonomous navigation in unstructured off-road environments is greatly improved by semantic scene understanding. Conventional image processing algorithms are difficult to implement and lack robustness due to a lack of structure and high variability across off-road environments. The use of neural networks and machine learning can overcome the previous challenges but they require large labeled data sets for training. In our work we propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation, without the need of any prior training data. The resulting segmented image is processed to extract, filter, and approximate objects as polygons, using a polygon approximation algorithm. The resulting polygons are then used to generate a semantic map of the environment. Using our framework. we show the capability to add new semantic classes in run-time for classification. The proposed methodology is also shown to operate in real-time and produce outputs at a frequency of 1Hz, using high resolution hyperspectral images.

翻译：在非结构化越野环境中的自主导航可借助语义场景理解得到显著提升。传统图像处理算法因缺乏结构化特征且越野环境变异性高而难以实现且鲁棒性不足。神经网络与机器学习可突破上述挑战，但需要大量标注数据集进行训练。本文提出基于高光谱图像的实时逐像素语义分类与分割方法，无需任何先验训练数据。通过多边形逼近算法对分割后图像进行处理，实现对象的提取、滤波与多边形近似。所得多边形被用于生成环境的语义地图。实验表明，本框架具备运行时动态添加新语义类别的分类能力，且能利用高分辨率高光谱图像以1Hz频率实时产生输出结果。