发表机构
University of Konstanz（康斯坦茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对嵌入式设备部署Visual SLAM的挑战，GLidE-SLAM提出单目混合间接-直接框架，通过架构分离，利用并行图像对齐操作及OpenGL ES 3.1计算着色器实现GPU加速直接跟踪，提升帧率，保持精度，改善在资源受限硬件上的部署。
AI 中文摘要
随着机器人技术、自主无人机和可穿戴扩展现实系统需求的增长，在嵌入式设备上部署视觉同步定位与地图构建（Visual SLAM）仍具有挑战性。跟踪必须保持高帧率，同时为地图扩展和维护保留计算资源。本文提出了GLidE-SLAM，这是一个单目混合间接-直接框架，通过架构分离来解决这一问题：系统在中间帧上执行GPU加速的直接跟踪，同时保留完整的间接管道用于地图扩展和全局一致性。我们利用高度并行的图像对齐操作进行仅姿态估计，无需深度优化或地图点创建，使工作量适合GPU卸载，并为后端任务释放CPU资源。我们使用与供应商无关的OpenGL ES 3.1计算着色器实现直接跟踪器，无需CUDA支持即可在更广泛的商用嵌入式平台上部署。据我们所知，这是第一个通过计算着色器为嵌入式类设备实现的完整直接光度姿态估计器。在目标平台上的实验表明，与仅使用CPU的基线相比，帧率提高了9倍，同时保持了轨迹精度，并改善了在商用资源受限硬件上的实际部署。
英文摘要
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.
Comments7 pages, 6 figures, 4 tables. Accepted for presentation at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
