Dataset Generation for Deep Visual Navigation in Unstructured Environments

Abstract

Visual navigation in unstructured environments is one of the essential functions of autonomous mobile robots. Most existing navigation methods are based on extracting traversable regions, and the visibility of the traversable region is crucial. However, in situations like plant-rich environments, such visibility is not guaranteed. A possible option is to directly infer the moving direction from images using deep learning techniques. Although this approach is promising because of a wide variety of application environments, obtaining a high-quality, large dataset will be an issue. This paper describes a data acquisition system that can easily collect various types of sensor data and a dataset generation method. We evaluate the generated datasets in several aspects. We also release the datasets, including image-path pairs and various raw sensor data.