Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, battery autonomy, available sensors, computing resources, processing time, etc.), and particularly in aerial vehicles, severely hamper the performance of fully-autonomous on-board control, including sensor processing and state estimation. In this work, we propose a spiking neural network (SNN) capable of estimating the pitch and roll angles of a quadrotor in highly dynamic movements from 6-degree of freedom Inertial Measurement Unit (IMU) data. With only 150 neurons and a limited training dataset obtained using a quadrotor in a real world setup, the network shows competitive results as compared to state-of-the-art, non-neuromorphic attitude estimators. The proposed architecture was successfully tested on the Loihi neuromorphic processor on-board a quadrotor to estimate the attitude when flying. Our results show the robustness of neuromorphic attitude estimation and pave the way towards energy-efficient, fully autonomous control of quadrotors with dedicated neuromorphic computing systems.

翻译：[translated abstract in Chinese] 已有令人信服的证据表明，神经形态处理器具有高能效和高更新率的特性，其性能超越了标准冯·诺依曼架构所能达到的水平。这种极具前景的特性在关键嵌入式系统（尤其是机器人领域）中可能具有显著优势。迄今为止，机器人（特别是空中飞行器）固有的诸多约束（如尺寸重量、电池续航、可用传感器、计算资源、处理时间等）严重限制了包括传感器处理与状态估计在内的全自主机载控制的性能。本研究提出一种脉冲神经网络（SNN），该网络能够根据六自由度惯性测量单元（IMU）数据，在高度动态运动中估计四旋翼飞行器的俯仰角和横滚角。该网络仅使用150个神经元，并借助真实环境中四旋翼飞行器获取的有限训练数据集，其表现已达到与非神经形态姿态估计器相媲美的水平。所提出的架构已成功在四旋翼飞行器机载的Loihi神经形态处理器上完成飞行过程中的姿态估计测试。实验结果表明，神经形态姿态估计具有良好的鲁棒性，为借助专用神经形态计算系统实现四旋翼飞行器高能效全自主控制奠定了基础。