LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices
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.
Growth and citations
This paper is currently showing No growth state computed yet..
Citation metrics and growth state from academic sources (e.g. Semantic Scholar). See About for details.
Cited by (0)
No citing papers yet
Papers that cite this one will appear here once data is available.
View citations page →References (0)
No references in DB yet
References for this paper will appear here once ingested.
Related papers in Image and Video Processing
- Physics-Based Learning of the Wave Speed Landscape in Complex Media0 citations
- Real-time topology-aware M-mode OCT segmentation for robotic deep anterior lamellar keratoplasty (DALK) guidance0 citations
- HypCBC: Domain-Invariant Hyperbolic Cross-Branch Consistency for Generalizable Medical Image Analysis0 citations
Growth transitions
No transitions recorded yet
Growth state transitions will appear here once computed.