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,通过语义场景理解可以大大提高自主导航能力。传统的图像处理算法由于缺乏结构性且在越野环境中变异性高,难以实现且缺乏鲁棒性。神经网络和机器学习的使用可以克服以前的挑战,但需要大量标记的数据集进行训练。在我们的工作中,我们提出了在没有先前训练数据的情况下,使用高光谱图像进行实时像素级语义分类和分割的方法。所得到的分割图像经过处理,使用边缘多边形拟合算法提取、过滤和近似对象,生成环境的语义地图.使用我们的框架,我们展示了在运行时添加新的语义类并进行分类的能力.所提出的方法也被证明可以在高分辨率高光谱图像上以1Hz的频率进行实时处理并产生输出。