Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive System
<|reference_start|>Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive System: 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.<|reference_end|>
@article{liu2024benchmarking,
    title={Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking
  Suite},
    author={Xinzhe Liu, Yuanyuan Yang, Bowen Xu, Delin Feng, S"oren Schwertfeger},
    journal={arXiv preprint arXiv:2406.17586},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17586},
    primaryClass={cs.RO}
}