This paper proposes a novel approach for robust plane matching and real-time RGB-D fusion based on the representation of plane parameter space. In contrast to previous planar-based SLAM algorithms estimating correspondences for each plane-pair independently, our method instead explores the holistic topology of all relevant planes. We note that by adopting the low-dimensionality parameter space representation, the plane matching can be intuitively reformulated and solved as a point cloud registration problem. Besides estimating the plane correspondences, we contribute an efficient optimization framework, which employs both frame-to-frame and frame-to-model planar consistency constraints. We propose a global plane map to dynamically represent the reconstructed scene and alleviate accumulation errors that exist in camera pose tracking. We validate the proposed algorithm on standard benchmark datasets and additional challenging real-world environments. The experimental results demonstrate its outperformance to current state-of-the-art methods in tracking robustness and reconstruction fidelity.

中文翻译：

---

通过嵌入式平面表示进行实时密集 3D 重建和相机跟踪

本文提出了一种基于平面参数空间表示的鲁棒平面匹配和实时 RGB-D 融合的新方法。与之前基于平面的 SLAM 算法独立估计每个平面对的对应关系相比，我们的方法反而探索所有相关平面的整体拓扑。我们注意到，通过采用低维参数空间表示，平面匹配可以直观地重新表述并解决为点云配准问题。除了估计平面对应之外，我们还提供了一个高效的优化框架，它同时采用了帧到帧和帧到模型的平面一致性约束。我们提出了一个全局平面图来动态表示重建的场景并减轻相机姿态跟踪中存在的累积误差。我们在标准基准数据集和其他具有挑战性的现实环境中验证了所提出的算法。实验结果表明，它在跟踪鲁棒性和重建保真度方面优于当前最先进的方法。