用侧扫声呐实现海藻农场自主导航定位与建图
Side Scan Sonar-based SLAM for Autonomous Algae Farm Monitoring
- 将声呐每帧检测的绳索点视为独立地标，提升建图精度
- 在真实海藻场测试中性能超越现有最优方案
- 适合从事水下机器人与智能农渔研究者使用
海藻规模化养殖需向智慧农业转型，依赖自主水下航行器（AUV）进行作物与结构巡检。当前部署瓶颈在于确保航行安全，需实时准确估计AUV位姿并构建基础设施地图。为此，我们提出一种高效的侧扫声呐（SSS）SLAM框架，通过将海藻农场结构绳索在后端建模为每帧声呐检测的独立地标序列，而非拼接为连续线段，从而更好地利用其几何特征。在真实海藻场进行的硬件在环（HIL）实验中，该方法性能优于现有最先进方案。代码与数据集已公开于https://github.com/julRusVal/sss_farm_slam。
原文摘要 · Abstract (English)
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.
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