轻量级视觉惯性里程计，可在低功耗设备上实时运行
LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices
- 采用并行化与低内存设计，适配嵌入式微控制器
- 在100mW功耗下实现20帧/秒的实时性能
- 开源实现，适合无人机和智能眼镜等资源受限场景
无需基础设施的高精度运动追踪系统对移动机器人和增强现实（AR）应用至关重要。然而，当前主流的视觉惯性里程计（VIO）系统计算开销过大，难以在微无人机、智能眼镜等资源受限设备上运行。本文提出LEVIO，一种专为超低功耗计算平台优化的完整VIO流程，支持六自由度（DoF）实时感知。该系统采用成熟的ORB特征跟踪与捆绑调整技术，同时强调计算高效架构，通过并行化与低内存占用设计，适配嵌入式微控制器及低功耗片上系统（SoC）。论文详细阐述算法设计选择与软硬件协同优化方法，并在资源受限硬件上验证了实时性能。实验基于支持并行处理的超低功耗RISC-V SoC，实现20帧/秒的处理速度，功耗低于100 mW。在公开的VIO数据集上进行基准测试，展现出效率与精度的良好平衡。为促进可复现性与应用推广，完整代码已开源。
原文摘要 · Abstract (English)
Accurate, infrastructure-less sensor systems for motion tracking are essential for mobile robotics and augmented reality (AR) applications. The most popular state-of-the-art visual-inertial odometry (VIO) systems, however, are too computationally demanding for resource-constrained hardware, such as micro-drones and smart glasses. This work presents LEVIO, a fully featured VIO pipeline optimized for ultra-low-power compute platforms, allowing six-degrees-of-freedom (DoF) real-time sensing. LEVIO incorporates established VIO components such as Oriented FAST and Rotated BRIEF (ORB) feature tracking and bundle adjustment, while emphasizing a computationally efficient architecture with parallelization and low memory usage to suit embedded microcontrollers and low-power systems-on-chip (SoCs). The paper proposes and details the algorithmic design choices and the hardware-software co-optimization approach, and presents real-time performance on resource-constrained hardware. LEVIO is validated on a parallel-processing ultra-low-power RISC-V SoC, achieving 20 FPS while consuming less than 100 mW, and benchmarked against public VIO datasets, offering a compelling balance between efficiency and accuracy. To facilitate reproducibility and adoption, the complete implementation is released as open-source.
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