Spiking monocular event based 6D pose estimation for space application

TL;DR

This paper introduces the first event-based dataset and a novel spiking neural network approach for 6D spacecraft pose estimation, demonstrating promising accuracy improvements for space applications.

Contribution

It presents the first event-based dataset SEENIC and a spiking neural network solution for spacecraft pose estimation, advancing fully event-based processing in space robotics.

Findings

Achieved 21cm position error

Achieved 14° rotation error

First event-based solution for spacecraft pose estimation

Abstract

With the growing interest in on On-orbit servicing (OOS) and Active Debris Removal (ADR) missions, spacecraft poses estimation algorithms are being developed using deep learning to improve the precision of this complex task and find the most efficient solution. With the advances of bio-inspired low-power solutions, such a spiking neural networks and event-based processing and cameras, and their recent work for space applications, we propose to investigate the feasibility of a fully event-based solution to improve event-based pose estimation for spacecraft. In this paper, we address the first event-based dataset SEENIC with real event frames captured by an event-based camera on a testbed. We show the methods and results of the first event-based solution for this use case, where our small spiking end-to-end network (S2E2) solution achieves interesting results over 21cm position error and 14degree rotation error, which is the first step towards fully event-based processing for embedded spacecraft pose estimation.