The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception

TL;DR

This paper introduces the GRIFFIN perception dataset, providing multi-sensor data for bird-scale flapping-wing robots to advance perception systems amidst flight-induced challenges.

Contribution

It presents the first comprehensive dataset for flapping-wing robot perception, including onboard sensors and ground truth data across diverse flight scenarios.

Findings

First dataset for flapping-wing robot perception.

Includes multi-sensor data capturing flight challenges.

Enables development of perception algorithms for bio-inspired robots.

Abstract

The development of automatic perception systems and techniques for bio-inspired flapping-wing robots is severely hampered by the high technical complexity of these platforms and the installation of onboard sensors and electronics. Besides, flapping-wing robot perception suffers from high vibration levels and abrupt movements during flight, which cause motion blur and strong changes in lighting conditions. This paper presents a perception dataset for bird-scale flapping-wing robots as a tool to help alleviate the aforementioned problems. The presented data include measurements from onboard sensors widely used in aerial robotics and suitable to deal with the perception challenges of flapping-wing robots, such as an event camera, a conventional camera, and two Inertial Measurement Units (IMUs), as well as ground truth measurements from a laser tracker or a motion capture system. A total of 21 datasets of different types of flights were collected in three different scenarios (one indoor and two outdoor). To the best of the authors' knowledge this is the first dataset for flapping-wing robot perception.