Vintr, Tomáš and Blaha, Jan and Rektoris, Martin and Ulrich, Jiří and Rouček, Tomáš and Broughton, George and Yan, Zhi and Krajník, Tomáš (2022) Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation. Frontiers in Robotics and AI, 9. ISSN 2296-9144
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Abstract
Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.
Item Type: | Article |
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Subjects: | OA STM Library > Mathematical Science |
Depositing User: | Unnamed user with email support@oastmlibrary.com |
Date Deposited: | 22 Jun 2023 07:02 |
Last Modified: | 12 Sep 2024 04:31 |
URI: | http://geographical.openscholararchive.com/id/eprint/1156 |