改进基于双天线GNSS/INS/气压计集成的车辆定向角度精度,使用自适应卡尔曼波器
Hongyuan Jiao1, Xiangbo Xu1, Shao Chen1
1School of Technology, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究利用全球导航卫星系统 (GNSS) 双天线,惯性导航系统 (INS) 和气压计数据的新融合,提高了自动驾驶车辆的定位准确性. 拟议的方法在各种环境中显著减少航向错误.
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:
- 准确的定向角度估计对于自动驾驶汽车的导航和定向决定至关重要.
- 现有的方法通常在具有挑战性的环境中难以准确,或依赖于单点GNSS解决方案.
研究的目的:
- 开发和验证一个强大的GNSS/INS/气表融合方法,用于高精度的自动驾驶汽车方向角估计.
- 与传统的扩展卡尔曼波器 (EKF) 方法相比,提高方向准确性.
主要方法:
- 全球导航卫星系统 (GNSS) 双天线,惯性导航系统 (INS) 和气表数据的整合.
- 适应卡尔曼波器 (AKF) 的实施,用于合INS错误和GNSS测量.
- 随机样本共识 (RANSAC) 的应用,以提高初始定位准确性和用于测量模型改进的动力约束.
主要成果:
- 在开放环境中达到0.5418°,在封闭环境中达到0.636°的偏向误差.
- 与EKF方法相比,在各自的环境中显著减少了37.62%和47.37%的错误.
- 通过实验结果验证了拟议的融合方法的有效性.
结论:
- 拟议的GNSS/INS/Barometer融合方法显著提高了自动驾驶汽车方向角的准确性.
- 整合RANSAC和动力学约束进一步提高了导航系统的稳定性和精度.
- 该方法显示出在各种环境条件下可靠的自动驾驶车辆操作的前景.
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