Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Peter Ross McAree"'
Publikováno v:
Sensors, Vol 23, Iss 6, p 3085 (2023)
The task of tracking the pose of an object with a known geometry from point cloud measurements arises in robot perception. It calls for a solution that is both accurate and robust, and can be computed at a rate that aligns with the needs of a control
Externí odkaz:
https://doaj.org/article/6ce6549013d7457db91a8d03c33c26de
Publikováno v:
Sensors, Vol 22, Iss 7, p 2687 (2022)
This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional
Externí odkaz:
https://doaj.org/article/f4f6f96e9dea4b6c9c830425d9afea4d
Publikováno v:
Sensors, Vol 21, Iss 6473, p 6473 (2021)
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 19
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 19
The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has sev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ce9b3abd93ec3352e3c93cd7587a04c
https://www.mdpi.com/1424-8220/21/19/6473
https://www.mdpi.com/1424-8220/21/19/6473
Publikováno v:
Advanced Engineering Informatics. 40:121-134
Predictive maintenance is the monitoring of an asset’s condition over its life cycle to provide a prognosis for when maintenance is required. Prior to prognosis, an asset’s life cycle is modeled by a health indicator (HI) which is derived from se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ba664648f580e2f3e4e3da19fa9107a
https://doi.org/10.1016/j.aei.2019.03.003
https://doi.org/10.1016/j.aei.2019.03.003
Publikováno v:
Journal of Field Robotics. 35:1182-1200
Registration, also know as extrinsic calibration, is the process of determining the position and orientation of a sensor relative to a known frame of reference. For ranging sensors such as light detection and ranging (LiDAR) used in field robotic app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::333d6a759f4a184c65563b3b86128c25
https://doi.org/10.1002/rob.21811
https://doi.org/10.1002/rob.21811
Publikováno v:
Journal of Field Robotics. 35:921-936
This paper addresses the problem of estimating object pose from high-density LiDAR measurements in unpredictable field robotic environments. Point-cloud measurements collected in such environments do not lend themselves to providing an initial estima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e324ccf43175679ee98585ea1eb3e7b9
https://doi.org/10.1002/rob.21788
https://doi.org/10.1002/rob.21788
Publikováno v:
Journal of Field Robotics. 34:985-1009
This paper describes the behavior of a commercial light detection and ranging LiDAR sensor in the presence of dust. This work is motivated by the need to develop perception systems that must operate where dust is present. This paper shows that the be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64be06202c4c5d7f2beeac74470cd875
https://doi.org/10.1002/rob.21701
https://doi.org/10.1002/rob.21701
Publikováno v:
Robotics and Autonomous Systems. 87:147-161
Many variants of the Iterative Closest Point (ICP) algorithm have been proposed for registering point clouds. This paper explores the performance of 20,736 ICP variants applied to the registration of point clouds for the purpose of terrain mapping, u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b63e8d0721ee3d2f47153d00bb9aa744
https://doi.org/10.1016/j.robot.2016.10.011
https://doi.org/10.1016/j.robot.2016.10.011
Publikováno v:
INDIN
This paper presents a machine learning-based dimension reduction framework (ML-framework). The ML-framework is designed to circumvent the challenges of high-dimensional discontinuous machine data applied in machine learning-based predictive maintenan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::458bb9d04ff71c29d857375114e03b31
https://doi.org/10.1109/indin41052.2019.8972020
https://doi.org/10.1109/indin41052.2019.8972020
Publikováno v:
INDIN
The unexpected failure of modern industrial systems is often managed using data-driven predictive maintenance (PdM) tools that continuously monitor a system’s health condition (HC) through raw sensor data to predict impending malfunction. However,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be1e75d29a7e992b2f2c17acaec68f6c
https://doi.org/10.1109/indin41052.2019.8972069
https://doi.org/10.1109/indin41052.2019.8972069