论文标题
更少的是更多：视觉大满贯的点稀疏
Keeping Less is More: Point Sparsification for Visual SLAM
论文作者
论文摘要
当将同时映射和本地化（SLAM）调整到现实世界应用程序（例如自动驾驶汽车，无人机和增强现实设备）时，其内存足迹和计算成本是限制性能和应用程序范围的两个主要因素。在基于稀疏功能的SLAM算法中，解决此问题的一种有效方法是通过选择可能对本地和全局捆绑捆绑调整（BA）有用的点来限制地图点大小。这项研究提出了用于大量系统中稀疏地图点的有效图优化。具体而言，我们将最大姿势可见度和最大空间多样性问题作为最小成本最大流量图优化问题。所提出的方法是现有SLAM系统的附加步骤，因此可以在常规或基于学习的SLAM系统中使用。通过广泛的实验评估，我们证明了所提出的方法以大约1/3的MAP点和1/2的计算实现了更准确的相机姿势。
When adapting Simultaneous Mapping and Localization (SLAM) to real-world applications, such as autonomous vehicles, drones, and augmented reality devices, its memory footprint and computing cost are the two main factors limiting the performance and the range of applications. In sparse feature based SLAM algorithms, one efficient way for this problem is to limit the map point size by selecting the points potentially useful for local and global bundle adjustment (BA). This study proposes an efficient graph optimization for sparsifying map points in SLAM systems. Specifically, we formulate a maximum pose-visibility and maximum spatial diversity problem as a minimum-cost maximum-flow graph optimization problem. The proposed method works as an additional step in existing SLAM systems, so it can be used in both conventional or learning based SLAM systems. By extensive experimental evaluations we demonstrate the proposed method achieves even more accurate camera poses with approximately 1/3 of the map points and 1/2 of the computation.
