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无人驾驶系统竞争 ROSbag 数据集

Lan Shi1, Younggil Chang2, Kylie Sommer-Kohrt1

  • 1School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA.

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概括
此摘要是机器生成的。

本研究详细介绍了一场IEEE无人机竞赛,旨在推进无人机 (UAV) 的自主导航. 竞赛使用模拟器,为开发强大的无人机自主性和无GPS导航系统提供了有价值的数据集.

关键词:
自主导航自主导航自主导航自主导航自主导航自主导航路径规划 路径规划 路径规划罗斯巴格 (Rosbag) 是一个红包包.斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯目标追踪 - 目标追踪

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科学领域:

  • 机器人技术和自主系统
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 无人驾驶飞行器 (UAV) 在机器人技术中对于自主导航和目标跟踪至关重要.
  • 开发强大的自主无人机算法需要大量的数据,模拟和测试,这些通常是资源密集的.

研究的目的:

  • 为提供IEEE主办的无人机竞赛的概述,专注于在复杂的,无GPS环境中推进无人机的自主性.
  • 引入一个数据集,包括来自模拟无人机的传感器数据.

主要方法:

  • 竞赛有两个挑战:Rover Chase (自动探测车) 和迷宫导航 (基于LiDAR的导航和避开障碍物).
  • 竞赛是在基于PX4-Gazebo的模拟器中进行的,以生成现实的传感器数据.
  • 该数据集包括采用囊格式的传感器数据:LiDAR,惯性测量单元 (IMU),GPS和遥测.

主要成果:

  • 该竞赛成功模拟了无人机复杂的自主导航任务.
  • 生成的数据集为对比和开发无人机自主算法提供了宝贵的资源.
  • 这些数据支持对感知和无GPS导航的研究,用于模拟和现实世界的应用.

结论:

  • IEEE无人机竞赛及其相关数据集对推进无人机自主性做出了重大贡献.
  • 这些发现促进了可重复的研究和开发更强大的无人机自主导航系统.
  • 该数据集对于研究人员在具有挑战性的环境中工作的感知,绘图和控制是有帮助的.