Point Cloud Simplification by Clustering for Robotics in Nuclear Decommissioning

  • Benjamin Bird

Student thesis: Master of Philosophy


Point Cloud Simplification by Clustering for Robotics in Nuclear DecommissioningBenjamin Jonathan BirdA thesis submitted to The University of Manchester for the degree of Master of Philosophy2015This thesis introduces new methods of point cloud simplification, which are specifically targeted at low power robotics applications, particularly those within the nuclear decommissioning industry. A review of existing point cloud simplifica- tion methods has been conducted, it is apparent that these exiting methods focus on maintain accuracy of the point cloud rather efficiency in terms of computa- tional resources. A selection of hardware for capturing 3D information regarding a 3D scene is evaluated based on performance, in several areas such as support- ing hardware requirements, resolution, power consumption and communication protocol. Testing shows the Hokuyo URG-04LX-UG01 to be the most suitable of the scanning hardware evaluated.The point cloud simplification methods proposed in this thesis are based on Vol- umetric Simplification of a live, polar coordinate based data stream. The various algorithms presented are then compared and evaluated based on their compu- tational time, accuracy, robustness to erroneous data, meshing performance and suitability for object recognition. The presented algorithms are then compared to a known, established clustering algorithm that is intended for use in embed- ded application. Comparisons are made based on computational time, visual performance, suitability for object recognition and suitability for meshing. The proposed algorithms are shown to be significantly superior in terms of computa- tional time and suitability for meshing.
Date of Award3 Jan 2016
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorBarry Lennox (Supervisor) & Simon Watson (Supervisor)


  • LiDAR
  • point cloud

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