cuVSLAM用CUDA加速，支持多相机实时位姿估计与建图。
cuVSLAM: CUDA accelerated visual odometry and mapping
- 基于CUDA优化，可在边缘设备上高效运行
- 支持1到32个相机任意布局，兼容多种传感器组合
- 在多个基准测试中表现领先，适合机器人实时定位
准确可靠的位姿估计是自主机器人的关键需求。我们提出cuVSLAM，一种先进的视觉同步定位与建图解决方案，可适配多种视觉-惯性传感器组合，包括多个RGB相机、深度相机和惯性测量单元。cuVSLAM支持从单个RGB相机到最多32个相机的任意几何配置，适用于广泛的机器人应用场景。该系统专门通过CUDA优化，可在NVIDIA Jetson等边缘计算设备上实现低计算开销的实时运行。本文介绍了cuVSLAM的设计与实现，展示了典型应用案例，并在多个前沿基准上进行了实证评估，结果表明其性能处于业界领先水平。
原文摘要 · Abstract (English)
Accurate and robust pose estimation is a key requirement for any autonomous robot. We present cuVSLAM, a state-of-the-art solution for visual simultaneous localization and mapping, which can operate with a variety of visual-inertial sensor suites, including multiple RGB and depth cameras, and inertial measurement units. cuVSLAM supports operation with as few as one RGB camera to as many as 32 cameras, in arbitrary geometric configurations, thus supporting a wide range of robotic setups. cuVSLAM is specifically optimized using CUDA to deploy in real-time applications with minimal computational overhead on edge-computing devices such as the NVIDIA Jetson. We present the design and implementation of cuVSLAM, example use cases, and empirical results on several state-of-the-art benchmarks demonstrating the best-in-class performance of cuVSLAM.
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