基于事件的视觉/惯性计数用于无人机室内导航
Ahmed Elamin1,2, Ahmed El-Rabbany1, Sunil Jacob3
1Civil Engineering Department, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了一种基于事件的视觉惯性测距系统,用于精确的室内导航. 这种方法大大减少了错误,并实现了实时性能,非常适合无人机等资源有限的平台.
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
- 传感器融合式传感器
背景情况:
- 室内导航是关键的,但由于GNSS信号不可用,因此具有挑战性.
- 事件摄像机提供高动态范围和低延迟,适合室内环境.
研究的目的:
- 为精确的室内导航开发一种基于事件的高效视觉惯性测距方法.
- 通过适应性事件积累和选择性关键框架更新来减少计算开销.
主要方法:
- 融合事件数据,标准图像和惯性测量.
- 在标准图像中检测和跟踪特征,使用累积事件进行跨跟踪.
- 通过合并IMU测量和特征轨迹来估计传感器状态.
主要成果:
- 与模拟中U-SLAM相比,实现了平均位置误差 (在x轴上高达50%) 和RMSE (在y轴上高达47%) 的大幅降低.
- 实时性能证明,每次事件批次的延迟时间为5-10毫秒,更新时间为10-20毫秒.
- 在模拟和现实世界数据集上均可验证.
结论:
- 拟议的方法为无人机室内导航提供了一个强大的解决方案.
- 基于事件的视觉惯性测距是准确和高效的室内定位的一个有前途的技术.
- 该方法适用于资源有限的平台.
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