最佳的低成本MEMSINS/GNSS 集成地缘引用解决方案用于LiDAR移动地图应用程序
Nasir Al-Shereiqi1, Mohammed El-Diasty1, Ghazi Al-Rawas1
1Civil and Architectural Engineering Department, College of Engineering, Sultan Qaboos University, Muscat 123, Oman.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究开发了一种低成本的基于微电子机械系统 (MEMS) 的惯性导航系统/全球导航卫星系统 (INS/GNSS) 地理参照系统. 新的无色化方法显著提高了LiDAR移动绘图的准确性,以满足地理空间数据生产标准.
科学领域:
- 地理学工程 工程地质学
- 机器人技术 机器人技术 机器人技术
- 传感器融合式传感器
背景情况:
- 使用LiDAR技术的移动绘图系统 (MMS) 对于在测量中生成准确的点云至关重要.
- 基于微电子机械系统 (MEMS) 的惯性导航系统/全球导航卫星系统 (INS/GNSS) 是MMS中的地理参考的关键,但受到噪音和偏差不稳定的影响.
- 现有的否定方法,如长短期记忆 (LSTM) - 循环神经网络 (RNN),在解决MEMS IMU数据质量方面存在局限性.
研究的目的:
- 开发一个低成本,准确的基于MEMS的INS/GNSS地理参照系统,用于LiDAR移动绘图.
- 引入新型的除和过技术,以提高MEMS IMU数据的准确性.
- 根据已建立的地理空间数据生产标准 (ASPRS) 验证系统的性能.
主要方法:
- 开发一个波形神经网络 (WNN) 用于拒绝MEMS IMU数据.
- 实施INS/GNSS集成的最佳最大概率估计器 (MLE).
- 拟议的WNN与LSTM-RNN模型的对比分析.
- 对地面和建筑物绘图场景的综合系统的准确性评估.
主要成果:
- 通过WNN解密方法,基于MEMS的INS/GNSS集成精度提高了约11%.
- 与没有GNSS的解决方案相比,最佳的MLE方法实现了~12%的更高准确度.
- 拟议的WNN取消超越了LSTM-RNN模型的性能.
- 导航解决方案的准确性在地面绘图时为1-3厘米,建筑绘图时为1-9厘米.
结论:
- 开发的WNN和最佳MLE方法有效地克服MEMS IMU噪声和偏差不稳定性.
- 拟议的系统实现了高精度,满足了ASPRS标准,用于各种绘图应用.
- 这项研究为LiDAR移动地图地理参考提供了一个具有成本效益和准确的解决方案.
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