GPU Accelerated Pose Graph Optimization for Stereo Visual SLAM

Yalcin H., ANKARALI M. M.

2026 European Control Conference, ECC 2026, Reykjavik, İzlanda, 7 - 10 Temmuz 2026, ss.2537-2542, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Reykjavik
  • Basıldığı Ülke: İzlanda
  • Sayfa Sayıları: ss.2537-2542
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

We explore a GPU-accelerated framework for real-time pose graph optimization in stereo camera-based Simultaneous Localization and Mapping (SLAM). As pose graphs increase in size, due to larger sliding windows and the integration of diverse sensor modalities, real-time optimization becomes challenging for traditional CPU-based solvers. Our approach exploits GPU parallelism to efficiently handle large-scale graphs, achieving optimization of graphs with over 10,000 edges. Each iteration takes only 3 milliseconds on an NVIDIA RTX 2060. Additionally, our work includes a Lie Algebra functions with clear documentation to support fast prototyping of GPU-based pose graph solvers. The full implementation is open-sourced at https://github.com/HaktanM/S-PGO.