  This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset
captured using a multi-sensor payload to promote the global navigation
satellite system (GNSS)-denied navigation research. The dataset features flight
distances ranging from 300m to 5km, collected using a DJI M600 hexacopter drone
and the National Research Council (NRC) Bell 412 Advanced Systems Research
Aircraft (ASRA). The dataset consists of hardware synchronized monocular
images, IMU measurements, 3D LiDAR point-clouds, and high-precision real-time
kinematic (RTK)-GNSS based ground truth. Ten datasets were collected as ROS
bags over 100 mins of outdoor environment footage ranging from urban areas,
highways, hillsides, prairies, and waterfronts. The datasets were collected to
facilitate the development of visual-inertial-LiDAR odometry and mapping
algorithms, visual-inertial navigation algorithms, object detection,
segmentation, and landing zone detection algorithms based upon real-world drone
and full-scale helicopter data. All the datasets contain raw sensor
measurements, hardware timestamps, and spatio-temporally aligned ground truth.
The intrinsic and extrinsic calibrations of the sensors are also provided along
with raw calibration datasets. A performance summary of state-of-the-art
methods applied on the datasets is also provided.
