论文标题
通过数据集的动态范围覆盖分析优化SLAM评估足迹
Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets
论文作者
论文摘要
同时本地化和映射（SLAM）由于在许多应用中的使用而被认为是不断发展的问题。 SLAM的评估通常使用公开可用的数据集进行，这些数据集的数量增加和难度水平。每个数据集提供一定水平的动态范围覆盖范围，这是测量SLAM的鲁棒性和弹性的关键方面。在本文中，我们根据许多表征指标提供了对数据集的动态范围覆盖范围的系统分析，我们的分析显示了数据集内和数据集之间的冗余水平。随后，我们提出了一种动态编程（DP）算法，以通过选择匹配单个或多个动态范围覆盖目标的序列的子集来消除SLAM评估过程中的冗余。结果表明，借助数据集表征和DP选择算法，可以在保持相同水平的覆盖范围的同时减少评估工作。我们还研究了如何利用提出的方法来优化现实世界大满贯系统的评估过程。
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.
