NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint

TL;DR

This paper introduces NTU VIRAL, a comprehensive multi-sensor dataset from an aerial vehicle, designed to advance research in autonomous aerial systems by providing diverse data in challenging environments.

Contribution

It presents a new, publicly available aerial dataset with synchronized visual, inertial, ranging, and lidar data, filling a gap in existing datasets for aerial autonomous research.

Findings

Dataset includes diverse indoor and outdoor scenarios.

Sensors are calibrated with high accuracy and synchronized.

Resources are openly accessible online.

Abstract

In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_dataset.