Multiple images have been widely used for scene understanding and navigation of unmanned ground vehicles in long term operations. However, as the amount of visual data in multiple images is huge, the cumulative error in many cases becomes untenable. This paper proposes a novel method that can extract features from a large dataset of multiple images efficiently. Then the membership ${K}$ -means clustering is used for high dimensional features, and the large dataset is divided into ${N}$ subdatasets to train ${N}$ conditional random field (CRF) models based on superpixel. A Softmax subdataset selector is used to decide which one of the ${N}$ CRF models is chosen as the prediction model for labeling images. Furthermore, some experiments are conducted to evaluate the feasibility and performance of the proposed approach.

中文翻译：

---

使用堆叠稀疏自动编码器和超像素 CRF 对 UGV 进行长期视觉场景理解

多幅图像已被广泛用于无人地面车辆在长期作业中的场景理解和导航。然而，由于多幅图像中的视觉数据量巨大，在很多情况下累积误差变得站不住脚。本文提出了一种新方法，可以有效地从多幅图像的大型数据集中提取特征。然后是会员 ${K}$ -means聚类用于高维特征，将大数据集划分为 ${N}$ 要训​​练的子数据集 ${N}$ 基于超像素的条件随机场 (CRF) 模型。Softmax 子数据集选择器用于决定哪一个 ${N}$ 选择 CRF 模型作为标记图像的预测模型。此外，还进行了一些实验来评估所提出方法的可行性和性能。