  Energy consumption of memory accesses dominates the compute energy in
energy-constrained robots which require a compact 3D map of the environment to
achieve autonomy. Recent mapping frameworks only focused on reducing the map
size while incurring significant memory usage during map construction due to
multi-pass processing of each depth image. In this work, we present a
memory-efficient continuous occupancy map, named GMMap, that accurately models
the 3D environment using a Gaussian Mixture Model (GMM). Memory-efficient GMMap
construction is enabled by the single-pass compression of depth images into
local GMMs which are directly fused together into a globally-consistent map. By
extending Gaussian Mixture Regression to model unexplored regions, occupancy
probability is directly computed from Gaussians. Using a low-power ARM Cortex
A57 CPU, GMMap can be constructed in real-time at up to 60 images per second.
Compared with prior works, GMMap maintains high accuracy while reducing the map
size by at least 56%, memory overhead by at least 88%, DRAM access by at least
78%, and energy consumption by at least 69%. Thus, GMMap enables real-time 3D
mapping on energy-constrained robots.
