InsSo3D：惯性导航系统与3D声呐SLAM用于浑浊环境检测
InsSo3D: Inertial Navigation System and 3D Sonar SLAM for turbid environment inspection
AI总结：
InsSo3D结合3D声呐和惯性导航系统，实现高效准确的水下SLAM，适用于浑浊环境中的结构检测。
摘要：
本文提出了InsSo3D，一种利用3D声呐和惯性导航系统（INS）进行大规模3D同时定位与建图（SLAM）的准确且高效的方法。与传统声呐不同，传统声呐产生包含距离和方位信息的2D图像，但缺乏高度信息，而3D声呐产生点云，因此不会产生高度歧义。我们引入了一种适应3D声呐数据的稳健且现代的SLAM框架，利用INS作为先验信息，检测环闭合并执行位姿图优化。我们在具有地面真实数据的测试水槽和户外被洪水淹没的采石场中评估了InsSo3D的性能。与参考轨迹和通过水下运动跟踪系统和视觉结构从运动（SFM）获得的地图相比，证明InsSo3D能够有效纠正里程计漂移。在50分钟的任务中，平均轨迹误差低于21厘米，生成一个10米乘20米的地图，平均重建误差为9厘米，能够安全地对自然或人工水下结构进行检测，即使在浑浊的水环境中。
英文摘要：
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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