The ability of living organisms to perform complex high speed manoeuvers in flight with a very small number of neurons and an incredibly low failure rate highlights the efficacy of these resource-constrained biological systems. Event-driven hardware has emerged, in recent years, as a promising avenue for implementing complex vision tasks in resource-constrained environments. Vision-based autonomous navigation and obstacle avoidance consists of several independent but related tasks such as optical flow estimation, depth estimation, Simultaneous Localization and Mapping (SLAM), object detection, and recognition. To ensure coherence between these tasks, it is imperative that they be trained on a single dataset. However, most existing datasets provide only a selected subset of the required data. This makes inter-network coherence difficult to achieve. Another limitation of existing datasets is the limited temporal resolution they provide. To address these limitations, we present FEDORA, a first-of-its-kind fully synthetic dataset for vision-based tasks, with ground truths for depth, pose, ego-motion, and optical flow. FEDORA is the first dataset to provide optical flow at three different frequencies - 10Hz, 25Hz, and 50Hz

翻译：生物体凭借极少量神经元和极低失效率完成复杂高速飞行机动能力，凸显了这类资源受限生物系统的有效性。事件驱动硬件近年来已成为在资源受限环境中实现复杂视觉任务的重要途径。基于视觉的自主导航与避障包含多个独立但相互关联的任务，如光流估计、深度估计、同步定位与地图构建(SLAM)、目标检测及识别。为确保这些任务间的协调一致性，必须使用单一数据集进行训练。然而现有大多数数据集仅提供所需数据的选择性子集，导致网络间协调难以实现。现有数据集的另一限制是其提供的时间分辨率有限。为解决上述问题，我们提出了FEDORA——首个面向视觉任务的完全合成数据集，包含深度、位姿、自运动及光流的真实标注。FEDORA是首个以10Hz、25Hz和50Hz三种不同频率提供光流的数据集。