Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response, we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Robust pose graph optimization is used to rigidly align the local submaps. As our representation is point based, map corrections can be performed efficiently without the need to store the entire history of input frames used for mapping as typically required by methods employing a grid based mapping structure. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking, mapping, and rendering accuracy when compared to existing dense neural RGBD SLAM methods. Project page: notchla.github.io/Loopy-SLAM.

翻译：神经RGBD SLAM技术在密集同时定位与地图构建（SLAM）领域展现出潜力，但仍面临相机跟踪过程中误差累积导致地图畸变的挑战。为此，我们提出Loopy-SLAM，该技术可全局优化相机位姿与密集三维模型。我们采用数据驱动的基于点的子地图生成方法实现帧到模型跟踪，并通过全局位置识别在线触发闭环校正。利用鲁棒的位姿图优化对局部子地图进行刚性对齐。由于本方法采用基于点的表征，地图校正可高效执行，无需像基于网格的建图结构方法那样存储用于建图的所有输入帧历史。在合成数据集Replica以及真实世界数据集TUM-RGBD和ScanNet上的评估表明，与现有密集神经RGBD SLAM方法相比，本方法在跟踪、建图和渲染精度方面具有竞争力或更优性能。项目页面：notchla.github.io/Loopy-SLAM。