Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite

TL;DR

The paper introduces SLAM Hive, a cloud-based benchmarking suite that enables large-scale, automated evaluation of SLAM algorithms across diverse datasets and configurations, facilitating comprehensive performance comparisons.

Contribution

SLAM Hive provides an open source, scalable platform for benchmarking SLAM algorithms using containerization and cloud deployment, addressing previous limitations in thorough evaluation.

Findings

Enables analysis of thousands of SLAM runs efficiently

Supports multiple sensor types and datasets

Facilitates comprehensive comparison of SLAM algorithms

Abstract

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.