一句话：提出软硬件协同系统 SuperNoVA，通过动态子图、资源虚拟化与专用加速器，在资源受限设备上实现高精度实时大规模 SLAM。
SLAM 对机器人、自动驾驶和 AR/VR 等应用至关重要，但在轻量设备上部署时面临高精度、实时性和资源效率的多重挑战。本文提出 SuperNoVA，一种从算法到硬件的全栈系统，面向资源受限场景实现高精度、实时、大规模 SLAM。其核心方法包括：动态构建满足延迟目标且保持精度的子图，虚拟化硬件资源以高效处理图结构，并设计新型硬件架构加速 SLAM 后端。评估表明，在大规模 AR 数据集上，SuperNoVA 相比乱序 CPU 基线将完整 SLAM 后端计算延迟降低 89.5%，相比嵌入式 GPU 基线降低 78.6%，同时将最大位姿误差较现有 SLAM 方案降低 89%，且始终满足延迟目标。
Simultaneous Localization and Mapping (SLAM) plays a crucial role in robotics, autonomous systems, and augmented and virtual reality (AR/VR) applications by enabling devices to understand and map unknown environments. However, deploying SLAM in AR/VR applications poses significant challenges, including the demand for high accuracy, real-time processing, and efficient resource utilization, especially on compact and lightweight devices. To address these challenges, we propose SuperNoVA, which enables high-accuracy, real-time, large-scale SLAM in resource-constrained settings through a full-stack system, spanning from algorithm to hardware. In particular, SuperNoVA dynamically constructs a subgraph to meet the latency target while preserving accuracy, virtualizes hardware resources for efficient graph processing, and implements a novel hardware architecture to accelerate the SLAM backend efficiently. Evaluation results demonstrate that, for a large-scale AR dataset, SuperNoVA reduces full SLAM backend computation latency by 89.5% compared to the baseline out-of-order CPU and 78.6% compared to the baseline embedded GPU, and reduces the maximum pose error by 89% over existing SLAM solutions, while always meeting the latency target.
@inproceedings{KimHNDS25,
title = {SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware SLAM},
author = {Seah Kim and Roger Hsiao and Borivoje Nikolic and James Demmel and Yakun Sophia Shao},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707258}
}