基于因子图和模糊逻辑的GPS/VIO集成导航系统.
1Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
Scientific reports
|December 27, 2024
概括
本研究介绍了一种基于图形的新型GPS/VIO系统,具有自适应特征流融合,在GPS中断时提高导航准确性. 该系统显著提高了自主应用的定位可靠性.
科学领域:
- 机器人技术和自主系统
- 导航和定位技术 导航和定位技术
- 计算机视觉和传感器融合
背景情况:
- 全球定位系统 (GPS) 和惯性导航系统 (INS) 的整合面临准确性和可靠性挑战,特别是在GPS信号中断期间.
- 传统GPS/INS系统中累积的错误限制了长期的导航解决方案.
- 视觉惯性口径测量 (VIO) 提供了互补的数据,但需要强大的特征处理.
研究的目的:
- 开发一种基于图形的新方法,将GPS和VIO整合起来,以提高导航和定位准确度.
- 引入适应性特征-流动融合,用于在各种环境中进行强大的运动估计和特征提取.
- 为了减轻与依赖GPS的导航系统相关的准确性和可靠性问题.
主要方法:
- 在GPS/VIO集成中,采用基于图形的方法,使用动态调节的模糊窗口.
- 适应性特征流动融合 (Adaptive Feature-Flow Fusion) 是为了增强运动估计和特征提取而开发的.
- 数据预处理,包括图像优化和IMU噪声降低,被纳入.
主要成果:
- 拟议的GPS/VIO系统显示了显著的精度改进,超过了扩展卡尔曼波器 (EKF).
- 在10s和27s的GPS中断期间,分别实现了84.59%和88.806%的精度提升.
- 通过数据预处理技术,预定义轨迹的准确性提高了44.7%.
结论:
- 新型基于图形的GPS/VIO系统有效地提高了导航和定位准确性,特别是在GPS中断期间.
- 适应性特征-流量融合在不同的特征环境中提供了强大的性能.
- 该系统的实时性能和准确性改进验证了其对自主应用的潜力.
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