用于自主藻类养殖场监测的基于侧扫声呐的SLAM
Side Scan Sonar-based SLAM for Autonomous Algae Farm Monitoring
AI总结：
针对海带养殖场内AUV安全导航的在线位姿与地图估计需求，提出一种基于侧扫声呐并将结构绳索建模为逐ping独立地标序列的高效SLAM框架，HIL实验表明其性能优于现有方法。
摘要：
将海藻养殖转变为工业规模的替代食物来源，依赖于通过类似于陆地农业的智慧养殖实现其流程自动化。这一过程的关键是自主水下航行器（AUV），因为它们能够使作物和结构检查自动化。然而，目前部署它们的瓶颈在于确保养殖场内的安全导航，这需要对AUV位姿和基础设施地图进行准确的在线估计。为实现这一点，我们提出了一种高效的基于侧扫声呐（SSS）的同步定位与地图构建（SLAM）框架，该框架利用海带养殖场的几何结构，在后端将结构绳索建模为来自每次SSS声波探测检测的独立地标序列，而不是将检测结果组合成细长表示。在真实AUV对海带养殖场进行勘测的硬件在环（HIL）实验中，我们的方法优于最先进的解决方案。该框架和数据集可在https://github.com/julRusVal/sss_farm_slam获取。
英文摘要：
The transition of seaweed farming to an alternative food source on an industrial scale relies on automating its processes through smart farming, equivalent to land agriculture. Key to this process are autonomous underwater vehicles (AUVs) via their capacity to automate crop and structural inspections. However, the current bottleneck for their deployment is ensuring safe navigation within farms, which requires an accurate, online estimate of the AUV pose and map of the infrastructure. To enable this, we propose an efficient side scan sonar-based (SSS) simultaneous localization and mapping (SLAM) framework that exploits the geometry of kelp farms via modeling structural ropes in the back-end as sequences of individual landmarks from each SSS ping detection, instead of combining detections into elongated representations. Our method outperforms state of the art solutions in hardware in the loop (HIL) experiments on a real AUV survey in a kelp farm. The framework and dataset can be found at https://github.com/julRusVal/sss_farm_slam.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
