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通过多源观测和对UVIO进行自适应优化来增强位估计算法.

Boya Zhang1, Gongliu Yang2, Jin Wang3,4,5

  • 1School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China.

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概括
此摘要是机器生成的。

本研究介绍了用于超宽带 (UWB) 位位置估计的自适应算法,通过使用多源数据和自适应优化来提高无人地面车辆 (UGV) 的准确性.

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在 LS-VCE 中.无人驾驶地面车辆无人驾驶地面车辆位置 位置 位置 位置多个观察的多个观察.这是一个小曲线.超宽带超宽带的使用

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 导航系统 导航系统
  • 传感器融合式传感器

背景情况:

  • 目前的超宽带 (UWB) 辅助视觉惯性测距 (VIO) 方案仅依赖于定位的距离测量.
  • 这种方法容易受到线性或低曲率轨迹的不准确性和多观察噪声的变化的影响.

研究的目的:

  • 为无人地面车辆 (UGV) 开发适应性UWB位置估计算法,以提高准确性和稳定性.
  • 在具有挑战性的轨迹场景和动态噪声条件下克服仅范围估计的局限性.

主要方法:

  • 一种新初始化方法,整合了距离,方程和高度测量.
  • 一个自适应的非线性优化算法,根据时间变化的噪声特征动态调整测量重量.

主要成果:

  • 拟议的算法在位估计方面表现出更好的稳定性和准确性.
  • 通过模拟和现实世界的UGV实验验证,证实了算法的有效性.

结论:

  • 开发的自适应性UWB位估计算法显著提高了UGV的定位精度.
  • 多源数据和自适应优化的集成为UWB辅助的VIO系统提供了更可靠的解决方案.