  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.
