CU-Multi: マルチロボットデータ関連付けのためのデータセット
CU-Multi: A Dataset for Multi-Robot Data Association
複数ロボットの観測データを時空間で対応付ける研究用に、コロラド大学ボルダー校で収集したRGB-D・GPS・LiDARを含むマルチロボットデータセットを構築した。
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著者: Doncey Albin, Miles Mena, Annika Thomas, Harel Biggie, Xuefei Sun, Dusty Woods, Steve McGuire, Christoffer Heckman
分類: cs.RO
原文アブストラクト
Multi-robot systems (MRSs) are valuable for tasks such as search and rescue due to their ability to coordinate over shared observations. A central challenge in these systems is aligning independently collected perception data across space and time, i.e., multi-robot data association. While recent advances in collaborative SLAM (C-SLAM), map merging, and inter-robot loop closure detection have significantly progressed the field, evaluation strategies still predominantly rely on splitting a single trajectory from single-robot SLAM datasets into multiple segments to simulate multiple robots. Without careful consideration to how a single trajectory is split, this approach will fail to capture realistic pose-dependent variation in observations of a scene inherent to multi-robot systems. To address this gap, we present CU-Multi, a multi-robot dataset collected over multiple days at two locations on the University of Colorado Boulder campus. Using a single robotic platform, we generate four synchronized runs with aligned start times and deliberate percentages of trajectory overlap. CU-Multi includes RGB-D, GPS with accurate geospatial heading, and semantically annotated LiDAR data. By introducing controlled variations in trajectory overlap and dense lidar annotations, CU-Multi offers a compelling alternative for evaluating methods in multi-robot data association. Instructions on accessing the dataset, support code, and the latest updates are publicly available at https://arpg.github.io/cumulti
