Online coverage and inspection planning for 3D modeling
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Online coverage and inspection planning for 3D modeling Soohwan Song1
· Daekyum Kim1
· Sungho Jo1
Received: 30 June 2019 / Accepted: 15 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage. Keywords Active sensing · Exploration planning · Autonomous inspection · Next-best-view · Motion planning
1 Introduction Reconstructed 3D models of large environments are becoming more useful in many industrial fields, including agriculture, engineering, and construction. With the development of various mobile robots, many studies suggest various methods to realize autonomous modeling systems (Blaer and Allen 2009; Ramanagopal et al. 2018; Roberts et al. 2017). Recently, because of rapid technological advances, MicroAerial Vehicles (MAVs) have become the most widely-used robots in the modeling systems. With their high maneuverability, MAVs can acquire information from almost any Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10514-020-09936-7) contains supplementary material, which is available to authorized users.
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Sungho Jo [email protected] Soohwan Song [email protected] Daekyum Kim [email protected]
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School of Computing, KAIST, Daejeon 34141, Yuseong-gu, Republic of Korea
vantage points. However, due to their limited battery life, it is important to efficiently plan viewpoints when modeling a target environment. The problem of computing the optimal trajectory of an MAV to reconstruct an environment is known as a view path planning problem. This problem is addressed differently depending on the availability of environment’s prior geometric information. First, when prior information
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