ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
This addresses the need for broadly applicable visual SLAM systems across diverse camera setups, representing a strong specific gain in the field.
The paper tackles the problem of real-time monocular visual SLAM without requiring camera intrinsics, achieving superior performance in camera tracking and dense 3D reconstruction compared to current methods, with a frontend model size reduced to 35% of comparable state-of-the-art approaches.
We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At its core, the system employs a lightweight symmetric two-view association (STA) model as the frontend, which simultaneously estimates relative camera poses and regresses local pointmaps from only two RGB images. This design reduces model complexity significantly, the size of our frontend is only 35\% that of comparable state-of-the-art methods, while enhancing the quality of two-view constraints used in the pipeline. In the backend, we construct a specially designed Sim(3) pose graph that incorporates loop closures to address accumulated drift. Extensive experiments demonstrate that our approach achieves superior performance in both camera tracking and dense 3D reconstruction quality compared to current methods. Github repository: https://github.com/zhangganlin/vista-slam