# LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices

arXiv: https://arxiv.org/abs/2602.03294 · HTML: https://mlsanity.com/paper/2602.03294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization](https://mlsanity.com/paper/1804.05625/) (2018)
- [Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones](https://mlsanity.com/paper/1809.05780/) (2018)
- [A Comparison of Modern General-Purpose Visual SLAM Approaches](https://mlsanity.com/paper/2107.07589/) (2021)
- [On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors](https://mlsanity.com/paper/2403.04071/) (2024)
- [Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation](https://mlsanity.com/paper/2305.13055/) (2023)
- [Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs](https://mlsanity.com/paper/2406.13345/) (2024)
- [PVI-DSO: Leveraging Planar Regularities for Direct Sparse Visual-Inertial Odometry](https://mlsanity.com/paper/2204.02635/) (2022)

## Built on by (most cited first, 1 of 1)

- [HexVIO: Towards All-Day Stereo-Inertial Tracking Through Commodity DSPs](https://mlsanity.com/paper/2610.03283/) (2026)

## Abstract (arXiv)

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.