FEDORA: Flying Event Dataset fOr Reactive behAvior

• URL: http://arxiv.org/abs/2305.14392v2
• Date: Fri, 15 Mar 2024 21:28:59 GMT
• Title: FEDORA: Flying Event Dataset fOr Reactive behAvior
• Authors: Amogh Joshi, Adarsh Kosta, Wachirawit Ponghiran, Manish Nagaraj, Kaushik Roy,
• Abstract summary: Event-based sensors have emerged as low latency and low energy alternatives to standard frame-based cameras for capturing high-speed motion. We present Flying Event dataset fOr Reactive behAviour (FEDORA) - a fully synthetic dataset for perception tasks.
• Score: 9.470870778715689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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