发表机构
Technical University of Munich; Munich Center for Machine Learning（慕尼黑工业大学; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
VkVIO利用Vulkan API实现跨平台GPU加速的视觉惯性里程计，在多种设备上达到最先进精度并优于CUDA系统，为机器人和XR提供低延迟、低功耗、低成本的VIO方案。
AI 中文摘要
机器人和扩展现实（XR）中的感知从根本上依赖于良好的状态估计。视觉惯性里程计（VIO）和视觉惯性同时定位与建图（VI-SLAM）是以经济高效且准确的方式实现这一目标的成熟方法。这些系统的效率使得设备可以更小、更凉爽、更轻便。GPU加速是降低延迟的自然途径，因为GPU在嵌入式计算机、手机和XR头显等平台上广泛可用。然而，以往文献中的相关工作仅限于使用CUDA进行此任务，这显著将部署选项限制为单一供应商。我们转而利用与供应商无关的Vulkan API，该API最初是为满足3D图形应用的严格性能要求而设计的。在这项工作中，我们提出了VkVIO，据我们所知，这是首个跨平台GPU加速的VIO方法。我们提供了最先进的精度，并满足实时操作所需的因果估计。我们将VkVIO部署在涵盖工作站、笔记本电脑和极其廉价的单板计算机等多种设备上，同时在相同硬件上优于基于CUDA的系统。VkVIO为机器人和XR中的低延迟、低功耗和低成本VIO提供了可能性。
英文摘要
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.
