概括
这项研究引入了无人驾驶车辆的自适应补充卡尔曼波器 (ACKF),通过使用生物极化传感器和MEMS惯性测量单元来提高姿态和方向精度,即使没有GPS. 在具有挑战性的环境中,ACKF方法显著提高了系统性能.
科学领域:
- 机器人技术和自主系统
- 传感器融合和导航
背景情况:
- 无人驾驶车辆需要可靠的态度和方向信息,特别是当全球导航卫星系统 (GNSS) 无法使用时.
- 现有的生物极化传感器 (PS) /MEMS惯性测量单元 (MIMU) 系统在恶劣环境 (倾斜,避难) 中扎,原因是传感器互补性的不足利用.
- 当前的方法限制了系统性能,因为它们不能充分利用陀螺仪,加速度计和PS的协同作用特性.
研究的目的:
- 为无人驾驶车辆提出改进的姿态和方向测量方法.
- 在具有挑战性的环境条件下提高系统适应性和准确性.
- 充分利用陀螺仪,加速度计和偏振传感器的互补性质.
主要方法:
- 开发一个适应补充卡尔曼波器 (ACKF) 用于态度和方向估计.
- 校正陀螺仪数据使用加速度计测量的重力,以提高姿态准确度.
- 通过卡尔曼最佳估计,IMU方向和倾斜补偿偏振方向的融合.
- 使用测量和理论重力之间的最大相关性来构建适应因子,以实现适应性传感器互补性.
主要成果:
- 拟议的ACKF方法在户外旋转和车辆阻塞测试中都表现出有效性.
- 在车辆测试中,与传统的卡尔曼过器相比,观察到距离 (89.3%),滚动 (93.2%) 和方向 (9.6%) 的根平均平方误差 (RMSE) 显著减少.
- 该方法在提高态度和方向信息的准确性和可靠性方面具有很大的优势.
结论:
- 适应补充卡尔曼波器 (ACKF) 有效地解决了GNSS拒绝的环境中现有的定位和方向确定方法的局限性.
- 通过自适应地融合PS,陀螺仪和加速度计的数据,ACKF提高了系统性能,提高了在恶劣条件下的稳定性.
- 这种方法为无人驾驶车辆的导航系统提供了重大进步,特别是在具有挑战性的操作场景下.
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