为应对传统声呐惯性系统在弱纹理水下环境中定位精度较低的问题，提出将声呐惯性滤波里程计应用于水下声呐惯性系统定位。依据声呐成像原理对声呐图像进行特征提取，运用迭代最近点算法（ICP）进行特征匹配。通过将点云匹配结果与IMU数据融合，提升状态估计的稳定性。这种方法使用IMU数据进行状态预测，并以声呐ICP匹配结果作为观测进行状态更新。最后将该滤波里程计与ICP顺序匹配结果一起纳入图优化框架进行位姿优化。试验表明，相比于传统方法，该方法的定位精度更高，能适应弱纹理环境。
To address the issue of low positioning accuracy of traditional sonar inertial systems in weakly textured underwater environments, it is proposed to apply sonar inertial filtering odometry to underwater sonar inertial system positioning. Based on the principle of sonar imaging, feature extraction is performed on sonar images, and iterative nearest point algorithm (ICP) is used for feature matching. By fusing the point cloud matching results with IMU data, the stability of state estimation is improved. This method uses IMU data for state prediction and updates the state based on sonar ICP matching results as observations. Finally, the filtered odometer and ICP sequential matching results will be incorporated into the graph optimization framework for pose optimization. The lake experiment shows that compared to traditional methods, this method has higher positioning accuracy and can adapt to weak texture environments.
2024,46(13): 146-149 收稿日期：2024-03-15
DOI：10.3404/j.issn.1672-7649.2024.13.026
分类号：TP242
基金项目：国家重点研发计划资助项目（2020YFC1521704）
作者简介：王波(1997-),男,硕士,研究方向为水下机器人
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