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.

翻译：精确且无需基础设施的传感器系统对于移动机器人和增强现实（AR）应用至关重要。然而，当前最先进的视觉惯性里程计（VIO）系统对资源受限的硬件（如微型无人机和智能眼镜）而言计算需求过高。本文提出了LEVIO，一个专为超低功耗计算平台优化的全功能VIO流程，可实现六自由度（DoF）的实时感知。LEVIO整合了成熟的VIO组件，如ORB特征跟踪与光束法平差，同时强调计算高效的架构，通过并行化和低内存占用以适应嵌入式微控制器和低功耗片上系统（SoC）。本文提出并详述了算法设计选择及软硬件协同优化方法，并展示了在资源受限硬件上的实时性能。LEVIO在一款并行处理的超低功耗RISC-V SoC上得到验证，能以低于100 mW的功耗实现20 FPS，并在公开VIO数据集上进行了基准测试，在效率与精度之间取得了出色的平衡。为促进可复现性与应用推广，完整实现已作为开源项目发布。