A RUGD Dataset for Autonomous Navigation and Visual Perception in Unstructured Outdoor Environments Research in autonomous driving has benefited from a number of visual datasets collected from mobile platforms, leading to improved visual perception, greater scene understanding, and ultimately higher intelligence. However, this set of existing data collectively represents only highly structured, urban environments. Operation in unstructured environments, e.g., humanitarian assistance and disaster relief or offroad navigation, bears little resemblance to these existing data. To address this gap, we introduce the Robot Unstructured Ground Driving (RUGD) dataset with video sequences captured from a small, unmanned mobile robot traversing in unstructured environments. Most notably, this data differs from existing autonomous driving benchmark data in that it contains significantly more terrain types, irregular class boundaries, minimal structured markings, and presents challenging visual properties often experienced in off road navigation, e.g., blurred frames. Over 7, 000 frames of pixelwise annotation are included with this dataset, and we perform an initial benchmark using stateoftheart semantic segmentation architectures to demonstrate the unique challenges this data introduces as it relates to navigation tasks.

Utilization of the open source datasets for semantic segmentation in automotive vision Great advancements in deeplearningbased machine vision are boosting the automotive industry potential to accurately recognize vehicle environment. The need for high quality datasets is of critical importance when training neural networks which are used to detect and classify objects in front of the vehicle. Available open source datasets for semantic segmentation can be used by a wide community of researchers to develop next generation selfdriving functions. Those datasets have severe limitations such as class imbalance, unobserved objects, erroneous labelling and limited number of covered scenarios. In this paper, we propose a sequence of steps to combine and manipulate existing open source datasets to maximize the inference performance. Those steps include relabeling with outlier removal, classdriven balancing of validation and training datasets, as well as targeted image manipulation for scarce classes. Our evaluation indicates the improved inference accuracy when compared to the usage of most common open source datasets.