Carlos A. Pinheiro de Sousa

arXiv2026

GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM

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.

cs.RO↗

arXiv2025

Co-Located VR with Hybrid SLAM-based HMD Tracking and Motion Capture Synchronization

We introduce a multi-user VR co-location framework that synchronizes users within a shared virtual environment aligned to physical space. Our approach combines a motion capture system with SLAM-based inside-out tracking to deliver smooth, high-framerate, low-latency performance. Previous methods either rely on continuous external tracking, which introduces latency and jitter, or on one-time calibration, which cannot correct drift over time. In contrast, our approach combines the responsiveness of local HMD SLAM tracking with the flexibility to realign to an external source when needed. It also supports real-time pose sharing across devices, ensuring consistent spatial alignment and engagement between users. Our evaluation demonstrates that our framework achieves the spatial accuracy required for natural multi-user interaction while offering improved comfort, scalability, and robustness over existing co-located VR solutions.

cs.RO↗