CNN-based Pose Estimation of a Non-Cooperative Spacecraft with Symmetries from Lidar Point Clouds

Abstract

Light detection and ranging (lidar) sensors provide accurate 3D point clouds for non-cooperative spacecraft pose estimation. Several robust methods such as Iterative Closest Point (ICP) exist to perform a local refinement of the pose starting from an initial estimate. However, finding the initial pose of the spacecraft is a global optimization problem which is challenging to solve in real-time. This is especially true on space hardware with limited computing power. In addition, many spacecrafts have a shape with multiple symmetries, making an unambiguous initial pose estimation impossible. This work introduces a Convolutional Neural Network (CNN) based pose estimation method, accounting for potential symmetries of the target satellite. The point clouds are projected to a 2D depth image before being processed by the network. To generate a sufficient amount of training data, a lidar simulator integrating multiple effects such as reflections or laser beam divergence is developed. While being trained solely on synthetic point clouds, the pose estimation method shows to be precise, efficient and reliable when evaluated on real point clouds taken at a hardware-in-the-loop rendezvous test facility. A runtime evaluation on potential space computing hardware is also performed to demonstrate the applicability of the method to real-time onboard pose estimation.