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

Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets