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.
Here is a video I have presented at IROS2021.