# VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan

arXiv: https://arxiv.org/abs/2609.30459 · HTML: https://mlsanity.com/paper/2609.30459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [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)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking](https://mlsanity.com/paper/2508.00088/) (2025)
- [Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO](https://mlsanity.com/paper/2003.13493/) (2020)
- [Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview](https://mlsanity.com/paper/2309.05445/) (2023)
- [From Virtual Reality to the Emerging Discipline of Perception Engineering](https://mlsanity.com/paper/2403.18588/) (2024)
- [High-Speed Stereo Visual SLAM for Low-Powered Computing Devices](https://mlsanity.com/paper/2410.04090/) (2024)
- [FastTrack: GPU-Accelerated Tracking for Visual SLAM](https://mlsanity.com/paper/2509.10757/) (2025)
- [GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM](https://mlsanity.com/paper/2607.16897/) (2026)
- [VkSplat: High-Performance 3DGS Training in Vulkan Compute](https://mlsanity.com/paper/2605.00219/) (2026)
- [Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization](https://mlsanity.com/paper/2607.06464/) (2026)

## Abstract (arXiv)

Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping (VI-SLAM) are proven ways of achieving this goal in a cost-effective and accurate manner. Efficiency in these systems allows for smaller, cooler, and lighter devices. GPU acceleration is a natural approach for reducing latency, thanks to their wide availability in platforms like embedded computers, mobile phones, and XR headsets. However, previous works in the literature have limited themselves to the use of CUDA for this task, significantly reducing deployment options to a single vendor. We instead leverage the vendor-agnostic Vulkan API, originally designed for the strict performance requirements of 3D graphics applications. In this work, we present VkVIO, the first, to the best of our knowledge, cross-platform GPU-accelerated VIO method. We provide state-of-the-art accuracy with causal estimates required for real-time operation. We deploy VkVIO on a diverse range of devices spanning a workstation, a laptop, and an extremely inexpensive single-board computer, while outperforming CUDA-based systems on the same hardware. VkVIO enables possibilities for low-latency, low-power, and low-cost VIO in robotics and XR.