MUN-FRL：用于空中自主导航与建图的视觉惯性激光雷达数据集
MUN-FRL: A Visual Inertial LiDAR Dataset for Aerial Autonomous Navigation and Mapping
AI总结：
该论文提出了一个多传感器空中视觉-惯性-激光雷达数据集，用于GNSS拒止环境下的自主导航与建图，涵盖多种飞行距离和环境，并提供标定和基准性能总结。
摘要：
本文介绍了一个独特的户外空中视觉-惯性-激光雷达数据集，该数据集使用多传感器载荷采集，旨在促进全球导航卫星系统（GNSS）拒止环境下的导航研究。数据集包含从300米到5公里不等的飞行距离，使用大疆M600六旋翼无人机和加拿大国家研究委员会（NRC）贝尔412先进系统研究飞机（ASRA）采集。数据集由硬件同步的单目图像、IMU测量值、3D激光雷达点云以及基于高精度实时动态（RTK）-GNSS的地面真值组成。共采集了十个数据集，以ROS包形式提供，涵盖超过100分钟的户外环境 footage，包括城市区域、高速公路、山坡、草原和水滨。这些数据集旨在促进基于真实无人机和全尺寸直升机数据的视觉-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、分割以及着陆区检测算法的开发。所有数据集包含原始传感器测量值、硬件时间戳以及时空对齐的地面真值。还提供了传感器的内参和外参标定以及原始标定数据集。此外，还提供了在数据集上应用的最先进方法的性能总结。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
