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Towards Robust and Efficient Plane Detection from 3D Point Cloud
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8870-6718
University of Massachusetts, Lowell, MA, USA.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2021 (English)In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 560-566Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a robust and scalable clustering method for 3D point-cloud plane segmentation with applications in Micro Aerial Vehicles (MAVs), such as Simultaneous Localization and Mapping (SLAM), collision avoidance, and object detection. Our approach builds on the sparse subspace clustering framework, which seeks a collection of subspaces that fit the data. Since subspace clustering requires solving a global sparse representation problem and forming a similarity graph, its high computational complexity is known to be a significant drawback, and performance is sensitive to a few hyperparameters. To tackle these challenges, our method has two key ingredients. We use randomized sampling to accelerate subspace clustering by solving a reduced optimization problem. We also analyze the obtained segmentation for quality assurance and performing a post-processing process to resolve two forms of model mismatch. We present numerical experiments to demonstrate the benefits and merits of our method. © 2021 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 560-566
Keywords [en]
Aircraft accidents, Antennas, Clustering algorithms, Numerical methods, Object detection, Quality assurance, Micro aerial vehicle, Numerical experiments, Optimization problems, Randomized sampling, Scalable clustering, Simultaneous localization and mapping, Sparse representation, Sub-Space Clustering, Micro air vehicle (MAV)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-86728DOI: 10.1109/ICUAS51884.2021.9476824Scopus ID: 2-s2.0-85111431557OAI: oai:DiVA.org:ltu-86728DiVA, id: diva2:1586003
Conference
2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021,15 June 2021-18 June 2021, Athens, Greece
Note

ISBN för värdpublikation:978-1-6654-4704-1, 978-1-6654-1535-4

Available from: 2021-08-18 Created: 2021-08-18 Last updated: 2021-08-18Bibliographically approved

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Mansouri, Sina SharifKanellakis, ChristoforosNikolakopoulos, George

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