Memory-Efficient Gaussian Fitting for Depth Images in Real Time
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2022_icra_spgf.pdf
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Author(s)
Li, Peter Zhi Xuan
Karaman, Sertac
Sze, Vivienne
Date Issued
May 23, 2022
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Citation
Karaman, Sertac, Sze, Vivienne and Li, Peter Zhi Xuan. 2022. "Memory-Efficient Gaussian Fitting for Depth Images in Real Time." IEEE International Conference on Robotics and Automation (ICRA).
Version
Author's final manuscript
Abstract
Computing consumes a significant portion of energy in many robotics applications, especially the ones involving
energy-constrained robots. In addition, memory access accounts for a significant portion of the computing energy. For mapping a 3D environment, prior approaches reduce the map size while incurring a large memory overhead used for storing sensor measurements and temporary variables during computation. In this work, we present a memory-efficient algorithm, named Single-Pass Gaussian Fitting (SPGF), that accurately constructs a compact Gaussian Mixture Model (GMM) which approximates measurements from a depthmap generated from
a depth camera. By incrementally constructing the GMM one pixel at a time in a single pass through the depthmap,
SPGF achieves higher throughput and orders-of-magnitude lower memory overhead than prior multi-pass approaches. By processing the depthmap row-by-row, SPGF exploits intrinsic properties of the camera to efficiently and accurately infer surface geometries, which leads to higher precision than prior approaches while maintaining the same compactness of the GMM. Using a low-power ARM Cortex-A57 CPU on the NVIDIA Jetson TX2 platform, SPGF operates at 32fps, requires 43KB of memory overhead, and consumes only 0.11J per frame (depthmap). Thus, SPGF enables real-time mapping of large 3D environments on energy-constrained robots.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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