CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments

• URL: http://arxiv.org/abs/2405.14731v1
• Date: Thu, 23 May 2024 15:59:48 GMT
• Title: CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments
• Authors: Yang Zhou, Long Quang, Carlos Nieto-Granda, Giuseppe Loianno,
• Abstract summary: This paper presents a pioneering and comprehensive real-world multi-robot collaborative perception dataset. It features raw sensor inputs, pose estimation, and optional high-level perception annotation. We believe this work will unlock the potential research of high-level scene understanding through multi-modal collaborative perception in multi-robot settings.
• Score: 8.177157078744571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past decade, although single-robot perception has made significant advancements, the exploration of multi-robot collaborative perception remains largely unexplored. This involves fusing compressed, intermittent, limited, heterogeneous, and asynchronous environmental information across multiple robots to enhance overall perception, despite challenges like sensor noise, occlusions, and sensor failures. One major hurdle has been the lack of real-world datasets. This paper presents a pioneering and comprehensive real-world multi-robot collaborative perception dataset to boost research in this area. Our dataset leverages the untapped potential of air-ground robot collaboration featuring distinct spatial viewpoints, complementary robot mobilities, coverage ranges, and sensor modalities. It features raw sensor inputs, pose estimation, and optional high-level perception annotation, thus accommodating diverse research interests. Compared to existing datasets predominantly designed for Simultaneous Localization and Mapping (SLAM), our setup ensures a diverse range and adequate overlap of sensor views to facilitate the study of multi-robot collaborative perception algorithms. We demonstrate the value of this dataset qualitatively through multiple collaborative perception tasks. We believe this work will unlock the potential research of high-level scene understanding through multi-modal collaborative perception in multi-robot settings.

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