# Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2604.04117 · HTML: https://mlsanity.com/paper/2604.04117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 5 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018)
- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Event-based Object Detection and Tracking for Space Situational Awareness](https://mlsanity.com/paper/1911.08730/) (2019)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware](https://mlsanity.com/paper/2406.11319/) (2024)

## Built on by (most cited first, 1 of 1)

- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)

## Abstract (arXiv)

Reliable relative pose estimation is a key enabler for autonomous rendezvous and proximity operations, yet space imagery is notoriously challenging due to extreme illumination, high contrast, and fast target motion. Event cameras provide asynchronous, change-driven measurements that can remain informative when frame-based imagery saturates or blurs, while neuromorphic processors can exploit sparse activations for low-latency, energy-efficient inferences. This paper presents a spacecraft 6-DoF pose-estimation pipeline that couples event-based vision with the BrainChip Akida neuromorphic processor. Using the SPADES dataset, we train compact MobileNet-style keypoint regression networks on lightweight event-frame representations, apply quantization-aware training (8/4-bit), and convert the models to Akida-compatible spiking neural networks. We benchmark three event representations and demonstrate real-time, low-power inference on Akida V1 hardware. We additionally design a heatmap-based model targeting Akida V2 and evaluate it on Akida Cloud, yielding improved pose accuracy. To our knowledge, this is the first end-to-end demonstration of spacecraft pose estimation running on Akida hardware, highlighting a practical route to low-latency, low-power perception for future autonomous space missions.