Abstract
It is challenging to automatically explore an unknown 3D environment with a robot only equipped with depth sensors due to the limited field of view. We introduce THP, a tensor field-based framework for efficient environment exploration which can better utilize the encoded depth information through the geometric characteristics of tensor fields. Specifically, a corresponding tensor field is constructed incrementally and guides the robot to formulate optimal global exploration paths and a collision-free local movement strategy. Degenerate points generated during the exploration are adopted as anchors to formulate a hierarchical TSP for global path optimization. This novel strategy can help the robot avoid long-distance round trips more effectively while maintaining scanning completeness. Furthermore, the tensor field also enables a local movement strategy to avoid collision based on particle advection. As a result, the framework can eliminate massive, time-consuming recalculations of local movement paths. We have experimentally evaluate our method with a ground robot in 8 complex indoor scenes. Our method can on average achieve 14% better exploration efficiency and 21% better exploration completeness than state-of-the-art alternatives using LiDAR scans. Moreover, compared to similar methods, our method makes path decisions 39% faster due to our hierarchical exploration strategy.
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Open datasets in the manuscript are from a public repository (https://niessner.github.io/Matterport/).
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Acknowledgements
We thank the anonymous reviewers for their valuable comments. We are grateful to Jiazhao Zhang and Chenyi Liu for the fruitful discussions.
Funding
This work is supported in part by the National Natural Science Foundation of China (62372457, 62002375, 62002376), Young Elite Scientists Sponsorship Program by CAST (2023QNRC001), and the Natural Science Foundation of Hunan Province of China (2021RC3071).
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Yuefeng Xi: Methodology, Writing Draft, Visualization, Results Analysis; Chenyang Zhu: Methodology, Supervision, Results Analysis; Yao Duan: Supervision, Results Analysis; Renjiao Yi: Supervision, Results Analysis; Lintao Zheng: Super-vision, Results Analysis; Hongjun He: Supervision, Results Analysis; Kai Xu: Methodology, Super-vision.
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The authors have no competing interests to declare that are relevant to the content of this article. The author Kai Xu is the Area Executive Editor of this journal.
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Yuefeng Xi received his B.E. degree in software engineering from Hebei University of Science and Technology. He is now a master student at the National University of Defense Technology (NUDT), China. His research interests cover robot perception.
Chenyang Zhu is an assistant professor at the School of Computing, NUDT. His current directions of interest include data-driven shape analysis and modeling, 3D vision, robot perception and navigation, etc.
Yao Duan is a Ph.D. candidate in the School of Computing, NUDT. Her current directions of interest include data-driven shape analysis and modeling, 3D vision and scene understanding, etc.
Renjiao Yi is an assistant professor in the School of Computing, NUDT. She is interested in 3D vision problems such as inverse rendering and image-based relighting.
Lintao Zheng is an assistant professor at the College of Meteorology and Oceanography, NUDT. He earned his Ph.D. degree in computer science from NUDT. His research interests focus on 3D vision and robot perception.
Hongjun He is a professor in the School of Computing, NUDT, where he received his Ph.D. degree in 1998. His current research interests are file system security, machine learning, and software measurement, etc.
Kai Xu is a professor in the School of Computing, NUDT, where he received his Ph.D. degree in 2011. He serves on the editorial boards of ACM Transactions on Graphics, Computer Graphics Forum, Computers & Graphics, etc.
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Xi, Y., Zhu, C., Duan, Y. et al. THP: Tensor-field-driven hierarchical path planning for autonomous scene exploration with depth sensors. Comp. Visual Media 10, 1121–1135 (2024). https://doi.org/10.1007/s41095-022-0312-6
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DOI: https://doi.org/10.1007/s41095-022-0312-6