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.

翻译：可靠相对位姿估计是实现自主交会与近距离操作的关键技术，然而太空图像因极端光照、高对比度及目标快速运动而面临显著挑战。事件相机提供异步、变化驱动的测量值，在基于帧的传统图像饱和或模糊时仍能保持信息有效；神经形态处理器则利用稀疏激活实现低延迟、高能效推理。本文提出一种融合事件视觉与BrainChip Akida神经形态处理器的航天器六自由度位姿估计链路。基于SPADES数据集，我们在轻量化事件帧表征上训练紧凑型MobileNet风格关键点回归网络，应用量化感知训练（8/4比特），并将模型转换为Akida兼容的脉冲神经网络。我们对比三种事件表征方案，在Akida V1硬件上实现实时低功耗推理。此外，我们设计了面向Akida V2的热力图模型，并在Akida云平台上评估，获得更高的位姿精度。据我们所知，这是首个在Akida硬件上运行的航天器位姿估计端到端演示，为未来自主太空任务中低延迟、低功耗感知提供了实用技术路径。