COSMO-Bench: A Benchmark for Collaborative SLAM Optimization

For each sequence we plot the reference solution and enumerate metadata including - The sequence name, source data, component trials, total duration (MM:SS), total distance traveled, and the number (#) of measurements plus outlier rate (%) for both intra-robot (LC) and inter-robot (IRLC) loop-closures (#, %). For each sequence we generate a dataset using both the Wi-Fi and Pro-Radio communication model for a total of 24 datasets. Component trial names are shortened for brevity - "D" for "Day" and "N" for "Night" for the MCD data and "K" for "Kittredge Loop" and "M" for "Main Campus" for the CU-Multi data. Note: Plots are not to scale. 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. T o address this gap, we design and release the C ollaborative O pen-Source M ulti-robot O ptimization Benchmark (COSMO-Bench) - a suite of 24 datasets derived from a baseline C-SLAM front-end and real-world LiDAR data.