Efficient LiDAR-based In-water Obstacle Detection and Segmentation by Autonomous Surface Vehicles in Aquatic Environments
Efficient LiDAR-based In-water Obstacle Detection and Segmentation by Autonomous Surface Vehicles in Aquatic Environments
复制标题
DOI：
10.1109/iros51168.2021.9636028
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Mingi Jeong;Alberto Quattrini Li
中科院分区：
文献类型：
--
作者：
Mingi Jeong;Alberto Quattrini Li
识别水中障碍物是自动水面车辆（asv）安全导航的基础。本文提出了一种从激光雷达传感器数据中分割单个水中物体（如游泳者、浮标、船只）和海岸线的无模型方法。为了减少计算量，我们的方法首先将三维点云转换为二维球面投影图像。然后，基于宽度优先搜索和层次聚类的一种变体相结合的算法，根据不同的目标对点进行分割。我们的方法解决了水域中点云的稀疏性和不稳定性，正如我们的实验所证明的那样，这一特性使得为自动驾驶汽车开发的方法不能直接适用于水中障碍物分割。我们的方法与其他最先进的方法进行了比较，并在模拟和现实世界的ASV部署中进行了验证，包括不同的对象和遇到的场景。该方法可以有效地实时分割未知的水中障碍物，优于其他最先进的方法。
Identifying in-water obstacles is fundamental for safe navigation of Autonomous Surface Vehicles (ASVs). This paper presents a model-free method for segmenting individual in-water objects (e.g., swimmers, buoys, boats) and shorelines from LiDAR sensor data. To reduce the computational requirement, our method first converts the 3D point cloud into a 2D spherical projection image. Then, an algorithm based on the integration of a breadth-first search and a variant of a hierarchical agglomerative clustering segments the points according to different objects. Our method addresses the sparsity and instability of the point cloud in the aquatic domain – a characteristic that makes the methods developed for self-driving cars not directly applicable for in-water obstacle segmentation, as demonstrated in our experiments. Our method is compared with other state-of-the-art approaches and is validated both in simulation and in real-world ASV deployments, with different objects and encountering scenarios. The proposed method is effective in segmenting in-water obstacles not known a priori, in real-time, outperforming other state-of-the art methods.