  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.
