ORCID Profile
0000-0002-6298-2340
Current Organisation
Queensland University of Technology
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Signal Processing | Aircraft Performance and Flight Control Systems | Electrical and Electronic Engineering | Computer Vision | Aerospace Engineering | Control Systems, Robotics and Automation
Air Passenger Transport | Air Safety | Air transport | Aerospace Transport not elsewhere classified |
Publisher: Springer Science and Business Media LLC
Date: 10-04-2010
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 11-2013
Publisher: SAGE Publications
Date: 22-02-2012
Abstract: In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular visual odometry and GPS measurements in a similar manner to a classic loosely coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map matching, feature tracking, visual loop closing, gravity vector or additional sensors such as an inertial measurement unit or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to the velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2017
Publisher: Wiley
Date: 11-04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: MDPI AG
Date: 08-10-2019
DOI: 10.3390/RS11192331
Abstract: Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training s les and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training s les and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For ex le, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.
Publisher: Pleiades Publishing Ltd
Date: 12-2007
Publisher: IEEE
Date: 06-2012
Publisher: Springer International Publishing
Date: 2015
Publisher: MDPI AG
Date: 05-02-2021
DOI: 10.3390/S21041115
Abstract: This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.
Publisher: Elsevier BV
Date: 06-2013
Publisher: Springer Science and Business Media LLC
Date: 14-01-2017
Publisher: IEEE
Date: 10-2006
Publisher: IEEE
Date: 04-2007
Publisher: IEEE
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: Springer International Publishing
Date: 2015
Publisher: Wiley
Date: 18-12-2012
DOI: 10.1002/ROB.20424
Publisher: Springer Science and Business Media LLC
Date: 07-08-2008
Publisher: IEEE
Date: 12-2011
Publisher: Wiley
Date: 23-03-2011
DOI: 10.1002/ROB.20387
Publisher: IEEE
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 12-08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Springer Science and Business Media LLC
Date: 04-09-2012
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 05-2014
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 03-2009
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 11-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 12-11-2009
Publisher: Wiley
Date: 07-02-2011
DOI: 10.1002/ROB.20359
Publisher: Elsevier BV
Date: 06-2013
Publisher: Springer Science and Business Media LLC
Date: 28-10-2011
Publisher: Wiley
Date: 03-2006
DOI: 10.1002/ROB.20115
Abstract: We present the design and implementation of a vision‐based feature tracking system for an autonomous helicopter. Visual sensing is used for estimating the position and velocity of features in the image plane (urban features like windows) in order to generate velocity references for the flight control. These visual‐based references are then combined with GPS‐positioning references to navigate towards these features and then track them. We present results from experimental flight trials, performed in two UAV systems and under different conditions, that show the feasibility and robustness of our approach. © 2006 Wiley Periodicals, Inc.
Publisher: Wiley
Date: 12-12-2013
DOI: 10.1002/ROB.21443
Publisher: Wiley
Date: 17-11-2015
DOI: 10.1002/ROB.21641
Publisher: Wiley
Date: 17-11-2015
DOI: 10.1002/ROB.21642
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 12-2011
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 14-11-2013
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 12-08-2012
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 05-2014
Publisher: Elsevier BV
Date: 2016
Start Date: 2012
End Date: 2014
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
End Date: 2011
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2012
End Date: 08-2015
Amount: $375,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 10-2010
End Date: 12-2013
Amount: $555,000.00
Funder: Australian Research Council
View Funded Activity