MUN-FRL:用于空中自主导航和绘图的视觉-惯性-激光雷达数据集
Ravindu G Thalagala1, Oscar De Silva1, Awantha Jayasiri2
1Intelligence Systems Lab, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada.
The International journal of robotics research
|April 10, 2025
概括
这项研究引入了一个新的空中数据集,用于全球导航卫星系统 (GNSS) 拒绝的导航研究,其中包括来自无人机和直升机的同步视觉惯性LiDAR数据. 本资源有助于开发先进的自主导航和感知算法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 航空航天工程 航空航天工程
背景情况:
- 全球导航卫星系统 (GNSS) 对于导航至关重要,但在某些环境中可能不可靠.
- 在无GNSS条件下为空中飞行器开发强大的自主导航系统是一个重大挑战.
- 现有的数据集可能缺乏先进算法开发所需的多样性或传感器融合丰富性.
研究的目的:
- 为推进无GNSS导航研究提供全面的多传感器空中数据集.
- 为算法开发提供同步的视觉惯性LiDAR数据,提供高精度的地面真相.
- 支持航程测量,绘图,物体检测和空中机器人着陆区识别方面的研究.
主要方法:
- 使用DJI-M600六旋翼无人机和NRC Bell412 ASRA收集数据.
- 集成的硬件同步传感器:单眼相机,惯性测量单元 (IMU) 和3D光检测和测距 (LiDAR).
- 获得了高精度实时动态 (RTK) -GNSS地面真相,用于九个不同的户外序列 (城市,农村,海).
主要成果:
- 一个独特的数据集,包含超过100分钟的机器人操作系统 (ROS) 包格式的飞行数据.
- 包括原始传感器测量,硬件时间和时空对齐的地面真相.
- 提供传感器校准数据和最先进算法的性能基准.
结论:
- 发布的数据集是空中机器人和自主导航研究人员的宝贵资源.
- 为 GNSS 拒绝环境开发和验证算法提供便利.
- 在视觉惯性LiDAR测距,绘图和感知任务中实现了进步.
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