GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM
Robotics
Summary
The authors created GLidE-SLAM, a system that helps robots and drones keep track of where they are using a single camera, even on small devices with limited computing power. They split the work so that quick, basic tracking uses the GPU, while the harder map-building tasks use the CPU. This method uses special graphics programming techniques that work on many devices, not just those with Nvidia GPUs. Their tests showed it runs much faster than older CPU-only methods without losing accuracy. This makes real-time tracking on everyday embedded devices more practical.
Authors
Carlos A. Pinheiro de Sousa, Heiko Hamann, Oliver Deussen
Abstract
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.