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.

翻译：在能量受限机器人中，内存访问的能耗占据了计算能耗的主导地位，这类机器人需要紧凑的3D环境地图以实现自主导航。现有建图框架仅关注减小地图体积，但因需要多轮处理每张深度图像，导致建图过程中内存消耗巨大。本文提出一种名为GMMap的内存高效连续占据地图，通过高斯混合模型精确建模3D环境。通过将深度图像单次压缩为局部高斯混合模型并直接融合为全局一致地图，实现了内存高效的GMMap构建。通过扩展高斯混合回归以建模未探索区域，可直接从高斯分量计算占据概率。在低功耗ARM Cortex A57 CPU上，GMMap可实现每秒最多60张图像的实时建图。与前期工作相比，GMMap在保持高精度的同时，将地图体积至少减少56%、内存开销至少减少88%、DRAM访问次数至少减少78%、能耗至少减少69%。因此，GMMap实现了能量受限机器人的实时3D建图。