用激光雷达与视觉融合，精准追踪近岸小船，提升自主船靠泊安全。
Near-Shore Mapping for Detection and Tracking of Vessels
- 结合激光雷达与图像数据，离线构建高精度近岸地图。
- 通过神经网络检测并过滤动态物体，避免误判静止障碍物。
- 在真实场景中验证，对小型船只跟踪效果显著，适合港口自治船应用。
为使自主水面船（ASV）完成靠泊，需精准追踪靠近码头的其他船只。皮划艇因体积小且紧邻码头，追踪难度大。传统海事目标追踪常使用陆地掩膜剔除陆地和码头，但陆地掩膜不精确时难以识别近码头目标。本文方法利用激光雷达（LiDAR）数据离线构建码头区域地图，3D测量精度高，可生成精确地图。但该过程可能将本应移动的物体误标为静态。为此，我们结合图像数据，通过训练好的神经网络实现视觉船舶检测与分割，自动识别并剔除潜在移动物体。在新采集的真实世界数据集上验证了方法有效性，该数据集包含皮划艇与日游船在靠近码头时与自主渡轮原型发生碰撞路径的多个序列。
原文摘要 · Abstract (English)
For an autonomous surface vessel (ASV) to dock, it must track other vessels close to the docking area. Kayaks present a particular challenge due to their proximity to the dock and relatively small size. Maritime target tracking has typically employed land masking to filter out land and the dock. However, imprecise land masking makes it difficult to track close-to-dock objects. Our approach uses Light Detection And Ranging (LiDAR) data and maps the docking area offline. The precise 3D measurements allow for precise map creation. However, the mapping could result in static, yet potentially moving, objects being mapped. We detect and filter out potentially moving objects from the LiDAR data by utilizing image data. The visual vessel detection and segmentation method is a neural network that is trained on our labeled data. Close-to-shore tracking improves with an accurate map and is demonstrated on a recently gathered real-world dataset. The dataset contains multiple sequences of a kayak and a day cruiser moving close to the dock, in a collision path with an autonomous ferry prototype.
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