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一种多传感器融合水下定位方法,该方法基于不香的卡尔曼过器在形上
Yang Wang1, Chenxi Xie1, Yinfeng Liu2
1Department of Automation, Beijing Information Science and Technology University, Beijing 102206, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了一种无气味卡尔曼波器 (UKF-M) 用于自动水下车辆 (AUV) 定位. UKF-M方法通过减少复杂的水下环境中的累积错误来提高轨迹估计的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 非线性系统分析 非线性系统分析
- 国家估计.
背景情况:
- 谎组和代数简化了机器人本地化非线性系统计算.
- 无气味卡尔曼波器 (UKF) 处理非线性,但在Lie组上面临高计算复杂性.
- 将李群应用于变形体,可以简化计算,同时保持几何约束.
研究的目的:
- 为了研究一个多传感器融合局部化方法,使用无气味的卡尔曼波器在多元体 (UKF-M).
- 提高自主水下车辆 (AUV) 在具有挑战性的水下环境中定位的准确性和稳定性.
主要方法:
- 为AUV开发了一个系统和多传感器模型.
- 为AUV系统设计了一个UKF-M.
- 实施了与UKF-M集成的多传感器融合战略.
主要成果:
- 拟议的UKF-M方法证明适用于水下环境.
- 该方法显著纠正了累积轨迹估计错误.
- 使用多传感器融合实现了精确的水下定位.
结论:
- UKF-M为AUV本地化提供了一种高效准确的方法.
- 多传感器聚合在分流器上有效地解决了水下导航的挑战.
- 该方法对提高AUV操作的可靠性显示出希望.
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