Abstract
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.
Abstract (translated)
准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）应用中的运动跟踪至关重要。然而，最先进的视觉惯性里程计（VIO）系统对资源受限硬件（如微型无人机和智能眼镜）来说计算需求过高。这项工作提出了LEVIO，这是一种针对超低功耗计算平台优化的全功能VIO流水线，能够实现六自由度（DoF）实时感知。 LEVIO融合了成熟的VIO组件，例如定向FAST与旋转BRIEF（ORB）特征跟踪和束调整技术，并且特别强调采用高效的并行化架构和低内存使用量设计，以适应嵌入式微控制器和低功耗系统级芯片（SoC）。论文详细介绍了算法的设计选择及硬件-软件协同优化方法，并展示了在资源受限硬件上的实时性能。 LEVIO在一个支持并行处理的超低能耗RISC-V SoC上进行了测试，在每秒20帧的速度下仅消耗不到100毫瓦，同时在公开的VIO数据集上与现有系统进行了基准测试。结果表明，LEVIO在效率和准确性之间提供了良好的平衡。 为了促进重现性和采用，LEVIO的完整实现已作为开源代码发布。
URL
https://arxiv.org/abs/2602.03294
