一个基于角加速的浮动探测器,用于移动无人机
Jiannan Zhao1, Quansheng Xie1, Feng Shuang1
1Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, China.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
一个新的A-LGMD模型使用图像角加速,以在无人机 (UAV) 中更快,更准确地检测迫在眉的物体. 这种方法通过减少虚假警报和预警延迟,改善了避开障碍的方法,这对于快速的无人机移动至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 无人机依靠视觉感知来避开障碍物.
- 快速的无人机移动挑战了当前的算法,导致错误报警和延迟警告,因为难以提取碰撞特征.
- 现有的方法在精确的特征提取和动态环境的时间序列建模方面扎.
研究的目的:
- 开发一种新型模型,用于在无人机中准确和快速检测迫在眉的物体.
- 解决当前算法在处理快速自动运动和视野变化的局限性.
- 提高微型和小型无人机中避障系统的可靠性.
主要方法:
- 提出了一种简洁的角球巨型运动探测器 (A-LGMD) 模型,其灵感来源于生物运动探测.
- 该模型利用图像角加速和更高阶的角大小信息来探测迫在眉的物体.
- 集成多个视野角度特征,以提高对背景运动干扰的稳定性.
主要成果:
- 该A-LGMD模型有效地检测图像的角加速,并过背景运动.
- 在合成和现实数据上的实验表明了早期预警能力.
- 该模型显示,与现有方法相比,精度提高,响应延迟减少.
结论:
- A-LGMD模型为无人机嵌入式碰撞检测提供了一个有希望的解决方案.
- 编码更高阶的角大小信息有效地解决了响应延迟问题.
- 该模型集成多种视觉线索的能力提高了其对运动干扰的抵抗力,这对于自主导航至关重要.
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