构建首个多机器人协同定位建图基准数据集，解决领域缺乏标准测试的问题。
COSMO-Bench: A Benchmark for Collaborative SLAM Optimization
- 基于真实激光雷达数据生成24组多机器人协同定位建图数据
- 提供可复现的基准测试环境，支持分布式优化算法对比
- 适合研究多机器人系统、协同定位与优化算法的学者和工程师
近年来，多机器人协同定位与建图（C-SLAM）的分布式优化算法成为研究热点。然而，该领域因缺乏标准化基准数据集而进展受限。单机器人SLAM领域已广泛使用基准数据集，多机器人研究同样亟需类似资源。为此，我们设计并发布了协同开源多机器人优化基准（COSMO-Bench）——一套由基础C-SLAM前端与真实激光雷达数据生成的24个数据集。该数据集支持算法性能评估与对比，推动多机器人协同优化研究的可复现性与进步。数据集可通过DOI：10.1184/R1/29652158 获取。
原文摘要 · Abstract (English)
Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). Research in this domain, however, is made difficult by a lack of standard benchmark datasets. Such datasets have been used to great effect in the field of single-robot SLAM, and researchers focused on multi-robot problems would benefit greatly from dedicated benchmark datasets. To address this gap, we design and release the Collaborative Open-Source Multi-robot Optimization Benchmark (COSMO-Bench) -- a suite of 24 datasets derived from a baseline C-SLAM front-end and real-world LiDAR data. Data DOI: https://doi.org/10.1184/R1/29652158
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