18 Papers
11 Citations
Thomas Lowe is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Point cloud & Computer science. The author has an hindex of 4, co-authored 17 publications.
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Papers
Digital Twin for the Future of Orchard Production Systems
Peyman Moghadam,Thomas Lowe,Everard J. Edwards +2 more
- 12 Feb 2020
TL;DR: The proposed AgScan3D+ system has been trialled in mango, macadamia, avocado and grapevines orchards and generated a digital-twin of 15,000 trees to enable improvement of production and dynamic prediction of disease, stress and yield gaps using an end-to-end AI platform.
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OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation
Fabio Ruetz,Emili Hernandez,Mark Pfeiffer,Helen Oleynikova,Mark Cox,Thomas Lowe,Paulo Vinicius Koerich Borges +6 more
- 20 May 2019
TL;DR: This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh), which represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot.
Canopy Density Estimation in Perennial Horticulture Crops Using 3D Spinning LiDAR SLAM
TL;DR: In this paper, a continuous-time 3D SLAM (Simultaneous Localisation and Mapping) algorithm was proposed to estimate the maximum likelihood canopy densities of a vineyard.
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Complementary Perception for Handheld SLAM
Thomas Lowe,Soohwan Kim,Mark Cox +2 more
- 23 Jan 2018
TL;DR: A key component of the proposed algorithm is the incorporation of depth uncertainty into visual features, which is effective for noisy surfaces and allows features with and without depth estimates to be modeled in a unified manner.
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Colourising Point Clouds Using Independent Cameras
Pavel Vechersky,Mark Cox,Paulo Vinicius Koerich Borges,Thomas Lowe +3 more
- 30 May 2018
TL;DR: This work investigates the problem of colourising a point cloud using an arbitrary mobile mapping device and an independent camera and demonstrates the “plug-and-play” portability of the resulting algorithm on data captured using sensor payloads mounted to hand-held, aerial, and ground systems.
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