用GPU加速的视觉定位系统，让嵌入式设备跑得更快更准。
GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM
- 分层设计：直接法在中间帧用GPU算位姿，间接法留着建图和全局优化。
- 实测帧率提升9倍，轨迹精度不降，适合资源受限的嵌入式设备。
- 用OpenGL ES通用着色器实现，无需CUDA，可部署在更多低端硬件上。
随着机器人、自主无人机和可穿戴扩展现实系统需求增长，视觉SLAM在嵌入式设备上的部署仍具挑战：追踪需保持高帧率，同时为建图与维护留出计算资源。本文提出GLidE-SLAM，一种单目混合间接-直接框架，通过架构分离实现：在中间帧上使用GPU加速的直接法进行位姿估计，而完整间接流程保留用于地图扩展与全局一致性维护。利用高度并行的图像对齐操作，仅进行位姿优化，不涉及深度优化或特征点生成，使负载适配GPU卸载，释放CPU资源供后端任务使用。直接追踪模块采用厂商无关的OpenGL ES 3.1计算着色器实现，可在无CUDA支持的主流嵌入式平台上部署。据我们所知，这是首个基于计算着色器在嵌入式设备上实现的完整直接光度位姿估计算法。在目标平台的实验表明，相较纯CPU基线，帧率最高提升9倍，同时保持轨迹精度，并显著提升在资源受限硬件上的实用性。
原文摘要 · Abstract (English)
With the growing demand for robotics, autonomous drones, and wearable extended reality systems, the deployment of Visual SLAM on embedded devices remains challenging. Tracking must sustain high frame rates while preserving compute resources for map extension and maintenance. This paper presents GLidE-SLAM, a monocular hybrid indirect-direct framework that addresses this by architectural separation: the system performs GPU-accelerated direct tracking on intermediate frames, while reserving the full indirect pipeline for map extension and global consistency. We leverage highly parallel image-alignment operations for pose-only estimation without depth optimization or map point creation, making the workload suitable for GPU offloading and freeing CPU resources for backend tasks. We implement the direct tracker using vendor-agnostic OpenGL ES~3.1 compute shaders, enabling deployment across a broader range of commodity embedded platforms without requiring CUDA support. To our knowledge, this is the first complete direct photometric pose estimator realized via compute shaders for embedded-class devices. Experiments on target platforms demonstrate up to 9$\times$ higher frame rates than the CPU-only baseline while maintaining trajectory accuracy and improving practical deployment across commodity resource-constrained hardware.
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