用3D声呐与惯性导航实现浑浊水下高精度建图
InsSo3D: Inertial Navigation System and 3D Sonar SLAM for turbid environment inspection
- 融合3D声呐点云与惯性数据，构建鲁棒SLAM框架
- 50分钟任务平均轨迹误差<21cm，重建误差仅9cm
- 适合浑浊水域中水下结构巡检，无需视觉条件
本文提出InsSo3D，一种基于3D声呐和惯性导航系统（INS）的高精度、高效大尺度3D同步定位与地图构建方法。与传统仅提供距离和方位信息的2D声呐不同，3D声呐生成三维点云，避免了俯仰模糊问题。该方法利用INS作为先验，针对3D声呐数据设计了现代且鲁棒的SLAM框架，支持回环检测与位姿图优化。在配备真实轨迹数据的测试水池及户外淹没采石场中评估，结果表明：相比水下运动追踪系统与视觉三维重建（SFM）获取的参考轨迹与地图，InsSo3D能有效校正里程计漂移。一次长达50分钟的任务中，平均轨迹误差低于21cm，生成10m×20m的地图，平均重建误差为9cm，可在浑浊水中安全完成天然或人工水下结构的巡检。
原文摘要 · Abstract (English)
This paper presents InsSo3D, an accurate and efficient method for large-scale 3D Simultaneous Localisation and Mapping (SLAM) using a 3D Sonar and an Inertial Navigation System (INS). Unlike traditional sonar, which produces 2D images containing range and azimuth information but lacks elevation information, 3D Sonar produces a 3D point cloud, which therefore does not suffer from elevation ambiguity. We introduce a robust and modern SLAM framework adapted to the 3D Sonar data using INS as prior, detecting loop closure and performing pose graph optimisation. We evaluated InsSo3D performance inside a test tank with access to ground truth data and in an outdoor flooded quarry. Comparisons to reference trajectories and maps obtained from an underwater motion tracking system and visual Structure From Motion (SFM) demonstrate that InsSo3D efficiently corrects odometry drift. The average trajectory error is below 21cm during a 50-minute-long mission, producing a map of 10m by 20m with a 9cm average reconstruction error, enabling safe inspection of natural or artificial underwater structures even in murky water conditions.
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