Autonomous void detection and characterisation in point clouds and triangular meshes. (1st August 2019)

• Record Type: Journal Article
• Authors: Bird, Benjamin; Wright, Thomas; Watson, Simon; Lennox, Barry
• Is Part Of: International journal of computational vision and robotics. Volume 9:Number 4(2019)
• Page Start: 368
• Page End: 386
• Publication Date: 2019-08-01
• Subjects: point cloud -- void detection -- meshing -- reconstruction -- computer vision; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

In this paper we propose and demonstrate a novel void characterisation algorithm which is able to distinguish between internal and external voids that are present in point clouds of both manifold and non-manifold objects and 3D scenes. We demonstrate the capabilities of our algorithm using several point clouds representing both scenes and objects. Our algorithm is shown in both a descriptive overview format as well as pseudocode. We also compare a variety of different void detection algorithms and then present a novel refinement to the best performing of these algorithms. Our refinement allows for voids in point clouds to be detected more efficiently, with fewer false positives and with over an order of magnitude improvement in terms of run time. We show our run time performance and compare it to results obtained using alternative algorithms, when tested using popular single board computers. This comparison is important as our work is intended for online robotics applications, where hardware is typically of low computational power. The target application for this work is 3D scene reconstruction to aid in the decommissioning of nuclear facilities.