Evaluating the performance of Simultaneous Localization and Mapping (SLAM) algorithms is essential for scientists and users of robotic systems alike. But there are a multitude different permutations of possible options of hardware setups and algorithm configurations, as well as different datasets and algorithms, such that it is infeasible to thoroughly compare SLAM systems against the full state of the art. To solve that we present the SLAM Hive Benchmarking Suite, which is able to analyze SLAM algorithms in thousands of mapping runs, through its utilization of container technology and deployment in the cloud. This paper presents the architecture and open source implementation of SLAM Hive and compares it to existing efforts on SLAM evaluation. We perform mapping runs of many of the most popular visual and LiDAR based SLAM algorithms against commonly used datasets and show how SLAM Hive and then be used to conveniently analyze the results against various aspects. Through this we envision that SLAM Hive can become an essential tool for proper comparisons and evaluations of SLAM algorithms and thus drive the scientific development in the research on SLAM. The open source software as well as a demo to show the live analysis of 100s of mapping runs can be found on our SLAM Hive website.

翻译：评估同步定位与建图（SLAM）算法的性能对机器人系统的科研人员与用户均至关重要。然而，硬件配置与算法参数存在大量可能的组合变体，加之不同的数据集与算法种类繁多，使得对SLAM系统进行全面且彻底的先进性能对比变得不可行。为此，我们提出了SLAM Hive基准测试套件，该系统通过容器化技术与云端部署，能够对SLAM算法进行数千次建图运行的规模化分析。本文介绍了SLAM Hive的体系架构与开源实现，并将其与现有的SLAM评估方案进行对比。我们基于常用数据集对多款主流视觉与激光雷达SLAM算法进行了批量建图测试，并演示了如何利用SLAM Hive便捷地从多维度对结果进行分析。我们期望SLAM Hive能成为SLAM算法严谨对比与评估的重要工具，从而推动SLAM领域的科研发展。开源软件及可实时分析数百次建图运行的演示系统均可在我们的SLAM Hive网站上获取。