高效的空间飞行器姿态估计：结合事件相机与神经形态硬件
Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware
AI总结：
本文提出一种结合事件相机与神经形态硬件的空间飞行器六自由度姿态估计方案，通过轻量事件-帧表示训练紧凑的MobileNet风格关键点回归网络，实现低功耗实时推理，展示了首个在Akida硬件上端到端运行的飞行器姿态估计演示。
摘要：
可靠的相对姿态估计是自主接近和近距离操作的关键使能技术，然而空间成像因极端光照、高对比度和快速目标运动而极具挑战性。事件相机提供异步、变化驱动的测量，可在帧成像饱和或模糊时仍保持信息性，而神经形态处理器可利用稀疏激活进行低延迟、低功耗推理。本文提出了一种结合事件视觉与BrainChip Akida神经形态处理器的空间飞行器6-DoF姿态估计流程。使用SPADES数据集，我们在轻量事件-帧表示上训练紧凑的MobileNet风格关键点回归网络，应用量化感知训练（8/4位），并将其转换为Akida兼容的脉冲神经网络。我们测试了三种事件表示，并在Akida V1硬件上实现了实时、低功耗推理。此外，我们设计了一种基于热图的模型针对Akida V2，并在Akida Cloud上评估，提高了姿态精度。据我们所知，这是首个在Akida硬件上端到端运行的空间飞行器姿态估计演示，展示了未来自主空间任务低延迟、低功耗感知的可行路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
