增强自动驾驶车辆定位使用松散合的INS/GNSS基于不变的EKF集成
Ahmed Ibrahim1, Ashraf Abosekeen1, Ahmed Azouz1
1Electrical Engineering Branch, Military Technical College (MTC), Cairo 11766, Egypt.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究通过将惯性导航系统 (INS) 与全球导航卫星系统 (GNSS) 集成使用不变扩展卡尔曼波器 (IEKF) 来增强自主车辆导航. IEKF显著提高了定位准确性,特别是在GNSS信号丢失时.
科学领域:
- 机器人技术和自主系统
- 导航和定位 导航和定位
- 信号处理 信号处理
背景情况:
- 高精度导航对于自动驾驶汽车至关重要.
- 全球导航卫星系统 (GNSS) 提供主要导航,但在某些环境中会导致信号退化.
- 惯性导航系统 (INS) 提供连续的导航,但随着时间的推移而漂移.
研究的目的:
- 引入和验证使用不变扩展卡尔曼波器 (IEKF) 的松散合的INS/GNSS集成方案.
- 解决基于传统扩展卡尔曼波器 (EKF) 的INS/GNSS集成的局限性,特别是在没有GNSS的场景中.
主要方法:
- 实施了一个松散合的INS/GNSS集成框架.
- 使用不变扩展卡尔曼波器 (IEKF),利用矩阵李组进行状态估计.
- 在现实道路轨迹上测试了拟议的系统.
主要成果:
- 基于IEKF的INS/GNSS集成显示了相对于传统EKF方法的显著性能提升.
- 在整体轨迹的2D位置中实现了82.98%的改进 根平均平方 (RMS) 错误 (19.4m到3.3m).
- 将二维位置的最大误差降低了80.78% (73.9m到14.2m).
结论:
- 与EKF方法相比,基于IEKF的INS/GNSS集成提供了更高的导航精度和稳定性.
- 这种方法在GNSS信号存在和阻塞条件下都有效,这对AV可靠性至关重要.
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