基于SO-CFAR和ADT特征提取的AUV SLAM方法
Xiaokai Mu1,2, Haiyang Chen2, Jiahao Wang2
1Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin, China.
Science progress
|October 3, 2024
概括
本研究将因子图优化SLAM应用于使用前声纳的自动水下车辆. 与死亡计算相比,该方法将本地化准确度提高了8.52%.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 计算机视觉 计算机视觉
背景情况:
- 自主水下车辆 (AUV) 需要强大的同时定位和映射 (SLAM) 导航.
- 面向前方的声纳提供了适合SLAM应用的先进检测功能.
研究的目的:
- 为了研究因子图优化SLAM的应用,使用AUV的特征图.
- 为了提高AUV在水下环境中的定位的准确性和可靠性.
主要方法:
- 从前声纳提取特征,使用联合的SO-CFAR和ADT噪声过.
- 通过加权代最接近点 (WICP) 算法进行特征点注册.
- 为SLAM实现因子图优化.
主要成果:
- 从声纳数据中成功提取特征点云.
- 使用WICP的功能有效注册.
- 在使用现场数据的基础平均平方误差 (RMSE) 与死亡计算 (DR) 相比,表现出了8.52%的改进.
结论:
- 提出的因子图优化SLAM方法对使用前声纳的AUV有效.
- SO-CFAR,ADT和WICP的整合显著提高了本地化准确性.
- 这种方法为复杂的水下环境中精确的AUV导航提供了可行的解决方案.
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