NR-SLAM：非刚性单目SLAM
NR-SLAM: Non-Rigid Monocular SLAM
AI总结：
本文提出NR-SLAM，结合动态变形图与粘弹性模型，在变形环境中实现单目SLAM的稀疏点云建图与相机运动估计，并在医学数据集上取得毫米级重建精度。
摘要：
本文提出了NR-SLAM，一种新颖的非刚性单目SLAM系统，其基础是将动态变形图与粘弹性变形模型相结合。前者使我们的系统能够在相机探索过程中表示变形环境的动力学，而后者使我们能够以简单的方式建模一般变形。所提出的系统能够在变形环境中自动初始化并扩展由稀疏点云建模的地图，并通过滑动窗口可变形光束法平差进行优化。该地图作为相机运动和变形估计的基础，使我们能够表示任意表面拓扑，克服了以往方法的局限性。为了评估我们的系统在具有挑战性的变形场景中的性能，我们在多个具有代表性的医学数据集上对其进行了评估。在我们的实验中，NR-SLAM优于以往的可变形SLAM系统，实现了毫米级重建精度，使自动化医疗干预更近一步。为了造福社区，我们公开了源代码。
英文摘要：
In this paper we present NR-SLAM, a novel non-rigid monocular SLAM system founded on the combination of a Dynamic Deformation Graph with a Visco-Elastic deformation model. The former enables our system to represent the dynamics of the deforming environment as the camera explores, while the later allows us to model general deformations in a simple way. The presented system is able to automatically initialize and extend a map modeled by a sparse point cloud in deforming environments, that is refined with a sliding-window Deformable Bundle Adjustment. This map serves as base for the estimation of the camera motion and deformation and enables us to represent arbitrary surface topologies, overcoming the limitations of previous methods. To assess the performance of our system in challenging deforming scenarios, we evaluate it in several representative medical datasets. In our experiments, NR-SLAM outperforms previous deformable SLAM systems, achieving millimeter reconstruction accuracy and bringing automated medical intervention closer. For the benefit of the community, we make the source code public.
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