{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T16:32:53Z","timestamp":1758040373476,"version":"3.44.0"},"reference-count":36,"publisher":"Cambridge University Press (CUP)","issue":"8","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, we propose a novel online informative path planner for 3-D modeling of unknown structures using micro aerial vehicles. Different from the <jats:italic>explore-then-exploit<\/jats:italic> strategy, our planner can cope with exploration and coverage simultaneously and thus obtain complete and high-quality 3-D models. We first devise a set of evaluation metrics considering the perception constraints of the sensor for efficiently evaluating the coverage quality of the reconstructed surfaces. Then, the coverage quality is utilized to guide the subsequent informative path planning. Specifically, our hierarchical planner consists of two planning stages \u2013 a local coverage stage for inspecting surfaces with low coverage quality and a global exploration stage for transiting the robot to unexplored regions at the global scale. The local coverage stage computes the coverage path that takes into account both the exploration and coverage objectives based on the estimated coverage quality and frontiers, and the global exploration stage maintains a sparse roadmap in the explored space to achieve fast global exploration. We conduct both simulated and real-world experiments to validate the proposed method. The results show that our planner outperforms the state-of-the-art algorithms and especially decreases the reconstruction error (at least 12.5% lower on average).<\/jats:p>","DOI":"10.1017\/s0263574725101860","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T03:48:34Z","timestamp":1753156114000},"page":"2855-2871","source":"Crossref","is-referenced-by-count":0,"title":["SEAC: a simultaneous exploration and coverage planner for online aerial 3-D modeling"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-9204","authenticated-orcid":false,"given":"Shiyong","family":"Zhang","sequence":"first","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuebo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianli","family":"Dong","sequence":"additional","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haobo","family":"Xi","sequence":"additional","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Nankai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoqun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4112-2250","authenticated-orcid":false,"given":"Jingjin","family":"Yu","sequence":"additional","affiliation":[{"name":"the State University of New Jersey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"S0263574725101860_ref32","doi-asserted-by":"crossref","unstructured":"[32] Jung, S. , Song, S. , Youn, P. and Myung, H. . \u201cMulti-layer Coverage Path Planner for Autonomous Structural Inspection of High-rise Structures.\u201d In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 1\u20139.","DOI":"10.1109\/IROS.2018.8593537"},{"key":"S0263574725101860_ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26054-9_23"},{"key":"S0263574725101860_ref11","doi-asserted-by":"crossref","unstructured":"[11] Tao, Y. , Wu, Y. , Li, B. , Cladera, F. , Zhou, A. , Thakur, D. and Kumar, V. . \u201cSEER: Safe Efficient Exploration for Aerial Robots Using Learning to Predict Information Gain.\u201d In: 2023 IEEE International Conference on Robotics and Automation (ICRA) (2023) pp. 1235\u20131241.","DOI":"10.1109\/ICRA48891.2023.10160295"},{"key":"S0263574725101860_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3104459"},{"key":"S0263574725101860_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3051563"},{"key":"S0263574725101860_ref17","doi-asserted-by":"crossref","unstructured":"[17] Roberts, M. , Shah, S. , Dey, D. , Truong, A. , Sinha, S. , Kapoor, A. , Hanrahan, P. and Joshi, N. . \u201cSubmodular Trajectory Optimization for Aerial 3D Scanning.\u201d In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017) pp. 5334\u20135343.","DOI":"10.1109\/ICCV.2017.569"},{"key":"S0263574725101860_ref31","doi-asserted-by":"crossref","unstructured":"[31] Lee, T. , Leok, M. and McClamroch, N. 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