Resumen
Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points.