Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly available datasets which are increasing in number and the level of difficulty. Each dataset provides a certain level of dynamic range coverage that is a key aspect of measuring the robustness and resilience of SLAM. In this paper, we provide a systematic analysis of the dynamic range coverage of datasets based on a number of characterization metrics, and our analysis shows a huge level of redundancy within and between datasets. Subsequently, we propose a dynamic programming (DP) algorithm for eliminating the redundancy in the evaluation process of SLAM by selecting a subset of sequences that matches a single or multiple dynamic range coverage objectives. It is shown that, with the help of dataset characterization and DP selection algorithm, a reduction in the evaluation effort can be achieved while maintaining the same level of coverage. We also study how the evaluation process of a real-world SLAM system can be optimized utilizing the method proposed.

翻译：同时定位与地图构建（SLAM）因其在众多应用中的使用而被视为一个不断演变的问题。SLAM的评估通常使用公开可用的数据集进行，这些数据集的数量和难度级别不断增加。每个数据集都提供一定程度的动态范围覆盖，这是衡量SLAM鲁棒性和抗干扰能力的关键方面。在本文中，我们基于多个表征指标对数据集的动态范围覆盖进行了系统分析，分析显示数据集内部及数据集之间存在大量冗余。随后，我们提出了一种动态规划（DP）算法，通过选择匹配单个或多个动态范围覆盖目标的序列子集来消除SLAM评估过程中的冗余。研究表明，借助数据集表征和DP选择算法，可以在保持相同覆盖水平的同时减少评估工作量。我们还研究了如何利用所提出的方法优化真实SLAM系统的评估过程。