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.
翻译:当将同步绘图和定位(SLAM)调整为实际应用时,如自主车辆、无人驾驶飞机和增强的现实装置等,其记忆足迹和计算成本是限制性能和应用范围的两个主要因素。在基于性能的SLAM算法中,这个问题的一个有效办法是通过选择对本地和全球捆绑调整可能有用的点来限制地图点的大小。本研究报告建议对SLAM系统中的分布式地图点进行高效的图形优化。具体地说,我们将一个最大面貌和最大空间多样性问题作为最低成本的最大流图优化问题,作为现有SLAM系统的一个额外步骤。拟议方法可以用于常规或学习性的SLAM系统。通过广泛的实验性评估,我们证明拟议方法能够以大约1/3的地图点和1/2的计算结果实现更精确的摄像器。