COSMO-Bench: A Benchmark for Collaborative SLAM Optimization

TL;DR

COSMO-Bench introduces a comprehensive benchmark suite of 24 datasets for evaluating collaborative SLAM optimization algorithms in multi-robot systems, addressing the lack of standardized datasets in this research area.

Contribution

This paper presents the first dedicated benchmark dataset collection for multi-robot C-SLAM, facilitating standardized evaluation and comparison of optimization algorithms.

Findings

Provides 24 real-world LiDAR-based datasets for C-SLAM

Enables consistent benchmarking of multi-robot SLAM algorithms

Supports future research in collaborative robot localization

Abstract

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