SESPnet: a lightweight network with attention mechanism for spacecraft pose estimation

Abstract

Spacecraft pose estimation plays an important role in an increasing number of on-orbit services: rendezvous and docking, formation flights, debris removal, and so on. Current solutions achieve excellent performance at the cost of a huge number of model parameters and are not applicable in space environments where computational resources are limited. In this paper, we present the Squeeze-and-Excitation based Spacecraft Pose Network (SESPNet). Our primary objective is to make a trade-off between minimizing model parameters and preserving performance to be more applicable to edge computing in space environments. Our contributions are primarily manifested in three aspects: first, we adapt the lightweight PeleeNet as the backbone network; second, we incorporate the SE attention mechanism to bolster the network’s feature extraction capabilities; third, we adopt the Smooth L1 loss function for position regression, which significantly enhances the accuracy of position estimation.