Efficient LiDAR-based In-water Obstacle Detection and Segmentation by Autonomous Surface Vehicles in Aquatic Environments

Mingi Jeong, Alberto Quattrini Li

Abstract: 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.