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.

翻译：本文介绍了一个独特的室外空中视觉-惯性-激光雷达数据集，该数据集利用多传感器载荷采集，旨在推动全球导航卫星系统拒止环境下的导航研究。该数据集采用大疆M600六旋翼无人机和加拿大国家研究委员会贝尔412先进系统研究飞机作为采集平台，飞行距离从300米到5公里不等。数据集包含硬件同步的单目图像、惯性测量单元数据、三维激光雷达点云数据，以及基于高精度实时动态差分全球导航卫星系统的真值。通过ROS数据包形式采集了10组数据集，总计超过100分钟的室外环境视频，涵盖城区、高速公路、山坡、草原和水岸等场景。该数据集旨在基于真实无人机和全尺寸直升机数据，促进视觉-惯性-激光雷达里程计与建图算法、视觉惯性导航算法、目标检测、分割以及着陆区域检测算法的研发。所有数据集均包含原始传感器测量值、硬件时间戳以及时空对齐的真值数据，同时提供传感器的内参标定和外参标定参数及原始标定数据集。本文还给出了当前先进方法在该数据集上的性能总结。