We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approaches rely on the three-dimensional (3D) Gaussian Mixture Model (GMM)-based representation of a 3D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy -- thereby, limiting flying duration and/or size of the drone due to a larger battery. Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3D map representation using a harmonic mean of "Gaussian-like" mixture (HMGM) model. The likelihood function useful for drone localization can be efficiently implemented by connecting many multi-input inverters in parallel, each programmed with the parameters of the 3D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D indoor localization dataset. The average localization error in our approach is $\sim$0.1125 m which is only slightly degraded than software-based evaluation ($\sim$0.08 m). Meanwhile, our localization framework is ultra-low-power, consuming as little as $\sim$17 $\mu$W power while processing a depth frame in 1.33 ms over hundred pose hypotheses in the particle-filtering (PF) algorithm used to localize the drone.

中文翻译：

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浮栅逆变器阵列上昆虫规模无人机的概率定位

我们提出了一种新颖的基于内存计算（CIM）的超低功耗框架，用于昆虫规模无人机的概率定位。传统的概率定位方法依赖于基于三维（3D）高斯混合模型（GMM）的3D地图表示。通常需要具有数百个混合函数的GMM模型来充分学习和表示地图的复杂性。同时，使用复杂的GMM地图模型进行定位需要大量的计算。由于昆虫规模的无人机在非常有限的面积/功率预算下运行，因此使用GMM模型进行的持续定位需要更高的运行能量-因此，由于电池更大，限制了飞行时间和/或无人机的大小。使用CIM方法解决昆虫规模的无人机本地化的计算难题，我们提出了一种使用“类高斯”混合（HMGM）模型的谐波均值的3D地图表示的新颖框架。通过并行连接许多多输入逆变器，可以有效地实现对无人机定位有用的似然函数，每个逆变器都使用表示为HMGM的3D地图模型的参数进行编程。当将深度测量值投影到实现的输入时，逆变器的总电流将模拟测量的可能性。我们已经在RGB-D室内定位数据集上描述了我们的方法。我们的方法中的平均本地化误差为$ \ sim $ 111.25 m，仅比基于软件的评估（$ \ sim $ 0.0m）稍有下降。同时，我们的本地化框架是超低功耗的，