无需相机参数的实时单目视觉定位系统，两图关联更高效
ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
- 用对称双视图关联模型，仅需两张图像估算位姿与局部点云
- 前端体积仅为同类方法的35%，且提升双视图约束质量
- 适合无标定相机或移动设备部署，追求轻量实时性者必看
我们提出ViSTA-SLAM，一种无需相机内参的实时单目视觉里程计系统，适用于多种相机配置。系统核心是轻量级对称双视图关联（STA）前端，仅通过两张RGB图像即可同时估计相对相机位姿并回归局部点云地图。该设计显著降低模型复杂度，前端大小仅为当前先进方法的35%，同时提升流水线中双视图约束的质量。后端构建了专用于Sim(3)的位姿图，并融合回环检测以缓解累积漂移。大量实验表明，本方法在相机跟踪和稠密3D重建质量上均优于现有方法。
原文摘要 · Abstract (English)
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
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