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The SLAM Hive Benchmarking Suite

2023-03-21 13:52:43
Yuanyuan Yang, Bowen Xu, Yinjie Li, Sören Schwertfeger


Benchmarking Simultaneous Localization and Mapping (SLAM) algorithms is important to scientists and users of robotic systems alike. But through their many configuration options in hardware and software, SLAM systems feature a vast parameter space that scientists up to now were not able to explore. The proposed SLAM Hive Benchmarking Suite is able to analyze SLAM algorithms in 1000's of mapping runs, through its utilization of container technology and deployment in a cluster. This paper presents the architecture and open source implementation of SLAM Hive and compares it to existing efforts on SLAM evaluation. Furthermore, we highlight the function of SLAM Hive by exploring some open source algorithms on public datasets in terms of accuracy. We compare the algorithms against each other and evaluate how parameters effect not only accuracy but also CPU and memory usage. Through this we show 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.

Abstract (translated)

对同时定位和地图(SLAM)算法进行基准测试对于科学家和机器人系统用户都非常重要。但是,由于硬件和软件中的许多配置选项,SLAM系统具有大量的参数空间,科学家至今无法探索。因此,提出的SLAM Hive基准测试套件能够通过使用容器技术和部署在一个集群中的方式,在数千次地图运行中对SLAM算法进行分析。本文介绍了SLAM Hive的架构和开源实现,并将其与现有的SLAM评估努力进行比较。此外,我们重点突出了SLAM Hive的功能,通过探索一些公共数据集上的开源算法,以精度为指标进行比较。我们比较了各种算法,并评估了参数不仅影响精度,还影响CPU和内存使用情况。通过这些比较,我们表明,SLAM Hive可以成为正确比较和评估SLAM算法的重要工具,从而推动SLAM研究中的科学发展。



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