Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite
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 of different permutations of possible options of hardware setups and algorithm configurations, as well as different datasets...
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| creator | Liu, Xinzhe Yang, Yuanyuan Xu, Bowen Feng, Delin Schwertfeger, Sören |
| description | 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 of different permutations of possible options of hardware
setups and algorithm configurations, as well as different datasets and
algorithms, such that it was previously 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 1000's 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 with popular visual, RGBD and LiDAR based SLAM algorithms against
commonly used datasets and show how SLAM Hive can 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
1000's of mapping runs can be found on our SLAM Hive website. |
| doi_str_mv | 10.48550/arxiv.2406.17586 |
| format | Article |
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algorithms is essential for scientists and users of robotic systems alike. But
there are a multitude of different permutations of possible options of hardware
setups and algorithm configurations, as well as different datasets and
algorithms, such that it was previously 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 1000's 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 with popular visual, RGBD and LiDAR based SLAM algorithms against
commonly used datasets and show how SLAM Hive can 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
1000's of mapping runs can be found on our SLAM Hive website.</description><identifier>DOI: 10.48550/arxiv.2406.17586</identifier><language>eng</language><subject>Computer Science - Robotics</subject><creationdate>2024-06</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2406.17586$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2406.17586$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Xinzhe</creatorcontrib><creatorcontrib>Yang, Yuanyuan</creatorcontrib><creatorcontrib>Xu, Bowen</creatorcontrib><creatorcontrib>Feng, Delin</creatorcontrib><creatorcontrib>Schwertfeger, Sören</creatorcontrib><title>Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite</title><description>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 of different permutations of possible options of hardware
setups and algorithm configurations, as well as different datasets and
algorithms, such that it was previously 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 1000's 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 with popular visual, RGBD and LiDAR based SLAM algorithms against
commonly used datasets and show how SLAM Hive can 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
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algorithms is essential for scientists and users of robotic systems alike. But
there are a multitude of different permutations of possible options of hardware
setups and algorithm configurations, as well as different datasets and
algorithms, such that it was previously 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 1000's 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 with popular visual, RGBD and LiDAR based SLAM algorithms against
commonly used datasets and show how SLAM Hive can 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
1000's of mapping runs can be found on our SLAM Hive website.</abstract><doi>10.48550/arxiv.2406.17586</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Robotics |
| title | Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite |
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