GMMap: Memory-Efficient Continuous Occupancy Map Using Gaussian Mixture Model
| Autor: | Li, Peter Zhi Xuan, Karaman, Sertac, Sze, Vivienne |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Robotics 40 (2024) 1339-1355 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/TRO.2023.3348305 |
| Popis: | 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. Comment: 17 pages, 12 figures, 3 tables |
| Databáze: | arXiv |
| Externí odkaz: |
|