基于INS/GNSS/UWB的ESKF动态和静态过的研究
Zongbin Ren1, Songlin Liu1, Jun Dai1,2
1Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种新的多源融合选算法,用于无人地面车辆 (UGV) 导航,使用多个传感器的错误状态卡尔曼波器 (ESKF) 提高定位准确性.
科学领域:
- 机器人技术和自主系统
- 导航和定位系统 导航和定位系统
- 传感器融合和数据集成
背景情况:
- 无人地面车辆 (UGV) 越来越依赖于多传感器融合,以实现强大的自主导航.
- 传统的联合过方法由于过器输出量之间的相互依赖性而存在局限性.
- 单传感器系统不足以满足UGV导航的复杂需求.
研究的目的:
- 为UGV定位状态估计提出一种新的动态和静态多源融合过算法.
- 提高导航准确度,克服现有的核聚变方法的局限性.
- 为UGV应用开发一个强大的和可通用的算法.
主要方法:
- 开发了一种使用错误状态卡尔曼过器 (ESKF) 的多源融合过算法.
- 集成惯性导航系统 (INS),全球导航卫星系统 (GNSS) 和超宽带 (UWB) 传感器.
- 实施了一种连续过方法,将动态ESKF (GNSS/INS) 和静态ESKF (UWB/INS) 结合起来.
主要成果:
- 提出的基于ESKF的融合方法显示了快速的趋同.
- 在定位精度方面取得了显著的改进:比松散合的GNSS/INS提高21.98%,比松散合的UWB/INS提高13.03%.
- 性能被证明受到动力ESKF传感器的精度和稳定性的影响.
结论:
- 这种新的算法为UGV导航提供了卓越的定位准确性和稳定性.
- 该方法表现出良好的通用性,插件功能和稳定性.
- 基于ESKF的融合是先进的UGV自主导航系统的一个有前途的方法.
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