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:针对机器人(如体积、重量、电池自主性、可用传感器、计算资源、处理时间等)、尤其是空中飞行器的固有限制,完全自主的控制在传感器处理和状态估计方面受到严重限制。神经形态处理器表现出高能效和更新速率的令人信服的证据,其性能超出了标准冯·诺伊曼架构所能实现的范围。这些有前途的特性在关键嵌入式系统,特别是机器人领域可能非常有优势。在本文中,我们提出了一个脉冲神经形态网络(SNN),能够通过6自由度惯性测量单元(IMU)数据从高度动态的四旋翼运动中估算俯仰和横滚角。仅使用150个神经元和通过四旋翼在实际环境中获得的有限训练数据集,该网络显示出与最先进、非神经形态姿态估算器相比有竞争力的结果。所提出的体系结构已成功测试,并运行在装载了Loihi神经形态处理器的四旋翼上,以估算飞行姿态。我们的结论表明,神经形态姿态估计具有高的鲁棒性,并为配备专用神经形态计算系统的四旋翼的能效高、完全自主控制铺平了道路。