用事件相机+神经形态芯片实现低功耗航天器实时位姿估计
Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware
- 结合事件相机与脑芯片硬件，设计端到端位姿估计算法
- 在Akida硬件上实现实时推理，功耗低于1.5瓦
- 适合对延迟和能耗敏感的深空探测任务
可靠的相对位姿估计是自主交会与近距操作的关键，但太空成像因极端光照、高对比度和快速目标运动而极具挑战。事件相机提供异步、变化驱动的测量，在帧图像饱和或模糊时仍保持信息有效；神经形态处理器可利用稀疏激活实现低延迟、低功耗推理。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6自由度位姿估计算法。基于SPADES数据集，训练轻量级类MobileNet关键点回归网络，采用8/4比特量化感知训练，并转换为Akida兼容的脉冲神经网络。评估三种事件表示方法，在Akida V1硬件上实现实时、低功耗推理。此外，针对Akida V2设计基于热力图的模型并在Akida Cloud上验证，提升位姿精度。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系统，展示了未来自主空间任务中低延迟、低功耗感知的可行路径。
原文摘要 · Abstract (English)
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.
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