The ability of resource-constrained biological systems such as fruitflies to perform complex and high-speed maneuvers in cluttered environments has been one of the prime sources of inspiration for developing vision-based autonomous systems. To emulate this capability, the perception pipeline of such systems must integrate information cues from tasks including optical flow and depth estimation, object detection and tracking, and segmentation, among others. However, the conventional approach of employing slow, synchronous inputs from standard frame-based cameras constrains these perception capabilities, particularly during high-speed maneuvers. Recently, event-based sensors have emerged as low latency and low energy alternatives to standard frame-based cameras for capturing high-speed motion, effectively speeding up perception and hence navigation. For coherence, all the perception tasks must be trained on the same input data. However, present-day datasets are curated mainly for a single or a handful of tasks and are limited in the rate of the provided ground truths. To address these limitations, we present Flying Event Dataset fOr Reactive behAviour (FEDORA) - a fully synthetic dataset for perception tasks, with raw data from frame-based cameras, event-based cameras, and Inertial Measurement Units (IMU), along with ground truths for depth, pose, and optical flow at a rate much higher than existing datasets.

翻译：资源受限的生物系统（如果蝇）能够在杂乱环境中执行复杂高速机动，这一能力一直是开发视觉自主系统的主要灵感来源。为模拟这种能力，此类系统的感知管道必须整合来自光流估计、深度估计、目标检测与跟踪、分割等任务的信息线索。然而，采用标准帧相机缓慢的同步输入的传统方法限制了这些感知能力，尤其在高速机动过程中。近年来，基于事件的传感器作为标准帧相机的低延迟、低能耗替代方案，被用于捕获高速运动，有效加速了感知乃至导航过程。为确保一致性，所有感知任务必须基于相同输入数据训练。但现有数据集主要为单个或少量任务设计，且真实标注的提供速率有限。为解决这些限制，我们提出面向反应行为的飞行事件数据集（FEDORA）——一个用于感知任务的完全合成数据集，包含来自帧相机、事件相机和惯性测量单元（IMU）的原始数据，以及高速率（远超现有数据集）的深度、位姿和光流真实标注。