This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation method that can be used to increase efficiency in the back-end optimization. Then we propose a buffer mechanism for the first time to improve the robustness of the segmentation. During the optimization, we use global information to optimize the frames with large error, and interpolation instead of optimization to update well-estimated frames to hierarchically allocate the amount of computation according to error of each frame. Comparative experiments on the benchmark show that our method greatly improves the efficiency of optimization with almost no drop in accuracy, and outperforms existing high-efficiency optimization method by a large margin.

翻译：本文为同步本地化和映射系统提供了一个基于等级的分区优化方法(SLAM) 。 首先,我们提出了一个可靠的轨迹分割法, 可以用来提高后端优化的效率。 然后,我们首次提出一个缓冲机制,以提高分割的稳健性。 在优化过程中,我们使用全球信息,用大错误优化框架,而不是优化更新高估框架,以便根据每个框架的错误按等级分配计算数量。 基准比较实验表明,我们的方法极大地提高了优化效率,几乎没有下降准确性,并且大大超过了现有的高效率优化方法。