Benchmarking of Pointnet-Based Architectures for Pose Estimation of Non-Cooperative Spacecraft from LiDAR Data
In the framework of space missions involving Close-Proximity Operations (CPO), accurate pose estimation between a chaser spacecraft and a non-cooperative resident space object remains a significant challenge. In such scenarios, pose determination must rely solely on Electro-Optical sensors such as cameras or LiDARs. Recently, Deep Learning (DL) approaches have gained significant attention for this task, demonstrating promising performance in terms of accuracy and runtime efficiency. This work presents a comparative analysis of both original and state-of-the-art point-based neural network architectures for LiDAR-based pose initialization of noncooperative spacecraft. The evaluated models, based on PointNet, differ in encoder adopted - PointNet (PN), a two-layer PointNet-based encoder (PN-PCN), and PointNet++ (PN++) - and in attitude parametrization, including quaternion, axisangle, 6D, and soft-label representations. The predicted pose is refined using the Iterative Closest Point (ICP) algorithm. Performance is assessed on synthetic scans of different targets generated by an in-house realistic LiDAR simulator. Results show that the PN-PCN encoder provides the best accuracyspeed trade-off, while soft-label parametrization yields the most accurate attitude estimation. With this configuration, subdegree-level attitude errors are achieved for Envisat and Aura, while degree-level attitude errors are obtained for Apollo, due to its more challenging symmetric geometry. Translational errors remain within a decimeter-level range across all targets.