ORCID Profile
0000-0002-5763-9644
Current Organisation
University of Technology Sydney
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Research Topics
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.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Adaptive Agents and Intelligent Robotics | Innovation and Technology Management | Manufacturing Engineering | Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics) | Control Systems, Robotics and Automation | Artificial Intelligence and Image Processing |
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in Engineering | Technological and Organisational Innovation
Related Links
Publications
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
Publisher: IEEE
Date: 24-10-2021
Combining multiple sensor modalities for a localisation robust to smoke
Publisher: IEEE
Date: 09-2011
A fast visual line segment tracker
Publisher: IEEE
Date: 09-2008
Sparse Pose Graph Optimization in Cycle Space
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Log-GPIS-MOP: A Unified Representation for Mapping, Odometry, and Planning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Selective combination of visual and thermal imaging for resilient localization in adverse conditions: Day and night, smoke and fire
Publisher: Wiley
Date: 24-05-2013
DOI: 10.1002/ROB.21464
A Tightly-Coupled Event-Inertial Odometry using Exponential Decay and Linear Preintegrated Measurements
Publisher: IEEE
Date: 23-10-2022
Semantic Keypoint Extraction for Scanned Animals using Multi-Depth-Camera Systems
Publisher: IEEE
Date: 29-05-2023
From the skin-depth equation to the inverse RFEC sensor model
Publisher: IEEE
Date: 11-2016
Active control for single camera SLAM
Publisher: IEEE
Date: 2006
Bayesian fusion using conditionally independent submaps for high resolution 2.5D mapping
Publisher: IEEE
Date: 05-2015
Information Driven Self-Calibration for Lidar-Inertial Systems
Publisher: IEEE
Date: 24-10-2020
Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain
Publisher: Elsevier BV
Date: 09-2011
Log-GPIS-MOP: A Unified Representation for Mapping, Odometry and Planning
Publisher: arXiv
Date: 2022
Loop-closure Candidates Selection By Exploiting Structure In Vehicle Trajectory
Publisher: IEEE
Date: 09-2011
Undelayed initialization of line segments in monocular SLAM
Publisher: IEEE
Date: 10-2009
Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/AN17795
Abstract: The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.
Computational pattern making from 3D garment models
Publisher: Association for Computing Machinery (ACM)
Date: 07-2022
Abstract: We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.
Modelling in-pipe acoustic signal propagation for condition assessment of multi-layer water pipelines
Publisher: IEEE
Date: 06-2015
Multiresolution mapping and informative path planning for UAV-based terrain monitoring
Publisher: IEEE
Date: 09-2017
Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Guiding and localising in real-time a mobile robot with a monocular camera in non-flat terrains
Publisher: Elsevier BV
Date: 2007
Probabilistic Dynamic Crowd Prediction for Social Navigation
Publisher: IEEE
Date: 30-05-2021
VirtualButcher: Coarse-to-fine Annotation Transfer of Cutting Lines on Noisy Point Cloud Reconstruction
Publisher: IEEE
Date: 18-11-2021
Faithful Euclidean Distance Field From Log-Gaussian Process Implicit Surfaces
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Continuous Integration over SO(3) for IMU Preintegration
Publisher: Robotics: Science and Systems Foundation
Date: 12-07-2021
Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Publisher: IEEE
Date: 05-2020
Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines
Publisher: Springer Science and Business Media LLC
Date: 09-09-2011
Probabilistic Plane Extraction and Modeling for Active Visual-Inertial Mapping
Publisher: IEEE
Date: 29-05-2023
Environment Modeling for Cooperative Aerial/Ground Robotic Systems
Publisher: Springer Berlin Heidelberg
Date: 2011
Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling
Publisher: IEEE
Date: 06-2014
IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Gaussian Mixture Marginal Distributions for Modelling Remaining Metallic Pipe Wall Thickness
Publisher: IEEE
Date: 11-2019
IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping
Publisher: arXiv
Date: 2019
Continuous Optimization Framework for Depth Sensor Viewpoint Selection
Publisher: Springer International Publishing
Date: 2020
Faithful Euclidean Distance Field from Log-Gaussian Process Implicit Surfaces
Publisher: arXiv
Date: 2020
GRAPH-SLAM BASED CALIBRATION OF AN EMBEDDED ASYNCHRONOUS MICROPHONE ARRAY FOR OUTDOOR ROBOTIC TARGET TRACKING
Publisher: WORLD SCIENTIFIC
Date: 25-08-2015
Active and Interactive Mapping with Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators
Publisher: arXiv
Date: 2020
Non-destructive evaluation of ferromagnetic material thickness using Pulsed Eddy Current sensor detector coil voltage decay rate
Publisher: Elsevier BV
Date: 12-2018
IDOL: A Framework for IMU-DVS Odometry using Lines
Publisher: IEEE
Date: 24-10-2021
An informative path planning framework for UAV-based terrain monitoring
Publisher: Springer Science and Business Media LLC
Date: 04-02-2020
DOI: 10.1007/S10514-020-09903-2
Abstract: Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps
Publisher: Field Robotics Publication Society
Date: 10-03-2022
DOI: 10.55417/FR.2022053
Abstract: Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection.
Split conditional independent mapping for sound source localisation with Inverse-Depth Parametrisation
Publisher: IEEE
Date: 10-2016
Asynchronous microphone arrays calibration and sound source tracking
Publisher: Springer Science and Business Media LLC
Date: 31-08-2019
IN2LAMA: INertial Lidar Localisation And Mapping
Publisher: IEEE
Date: 05-2019
Coupling conditionally independent submaps for large-scale 2.5D mapping with Gaussian Markov Random Fields
Publisher: IEEE
Date: 05-2017
Gaussian Process Preintegration for Inertial-Aided State Estimation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Predicting Objective Function Change in Pose-Graph Optimization
Publisher: IEEE
Date: 10-2019
Automatic detection and verification of pipeline construction features with multi-modal data
Publisher: IEEE
Date: 09-2014
Towards real-time 3D sound sources mapping with linear microphone arrays
Publisher: IEEE
Date: 05-2017
Path planning with stability uncertainty for articulated mobile vehicles in challenging environments
Publisher: IEEE
Date: 09-2014
Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data
Publisher: MDPI AG
Date: 06-10-2017
DOI: 10.3390/S17102276
Active and Interactive Mapping With Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Fusing Monocular Information in Multicamera SLAM
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
Action Selection For Single-camera Slam
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2010
Robust Incremental SLAM Under Constrained Optimization Formulation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Lean meat yield estimation using a prototype 3D imaging approach
Publisher: Elsevier BV
Date: 11-2021
Observability driven Multi-modal Line-scan Camera Calibration
Publisher: IEEE
Date: 14-09-2020
Continuous-Time Gaussian Process Motion-Compensation for Event-Vision Pattern Tracking with Distance Fields
Publisher: IEEE
Date: 29-05-2023
SPaM: soft patch matching for non-rigid pointcloud registration
Publisher: Frontiers Media SA
Date: 17-07-2023
DOI: 10.3389/FROBT.2023.1019579
Abstract: 3d reconstruction of deformable objects in dynamic scenes forms the fundamental basis of many robotic applications. Existing mesh-based approaches compromise registration accuracy, and lose important details due to interpolation and smoothing. Additionally, existing non-rigid registration techniques struggle with unindexed points and disconnected manifolds. We propose a novel non-rigid registration framework for raw, unstructured, deformable point clouds purely based on geometric features. The global non-rigid deformation of an object is formulated as an aggregation of locally rigid transformations. The concept of locality is embodied in soft patches described by geometrical properties based on SHOT descriptor and its neighborhood. By considering the confidence score of pairwise association between soft patches of two scans (not necessarily consecutive), a computed similarity matrix serves as the seed to grow a correspondence graph which leverages rigidity terms defined in As-Rigid-As-Possible for pruning and optimization. Experiments on simulated and publicly available datasets demonstrate the capability of the proposed approach to cope with large deformations blended with numerous missing parts in the scan process.
Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression
Publisher: IEEE
Date: 23-05-2022
Incremental SQP method for constrained optimization formulation in SLAM
Publisher: IEEE
Date: 11-2016
Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression
Publisher: arXiv
Date: 2022
Learning spatial correlations for Bayesian fusion in pipe thickness mapping
Publisher: IEEE
Date: 05-2014
Conditions for suboptimal filter stability in SLAM
Publisher: IEEE
Date: 2004
Estimator stability analysis in SLAM
Publisher: Elsevier BV
Date: 07-2004
Socially Constrained Tracking in Crowded Environments Using Shoulder Pose Estimates
Publisher: IEEE
Date: 05-2018
Topological Trajectory Prediction with Homotopy Classes
Publisher: IEEE
Date: 29-05-2023
Simultaneous asynchronous microphone array calibration and sound source localisation
Publisher: IEEE
Date: 09-2015
Clarifying clairvoyance: Analysis of forecasting models for near-sinusoidal periodic motion as applied to AUVs in shallow bathymetry
Publisher: Elsevier BV
Date: 10-2019
Multimodal localization: Stereo over LiDAR map
Publisher: Wiley
Date: 21-01-2020
DOI: 10.1002/ROB.21936
Unscented Transformation of Vehicle States in SLAM
Publisher: IEEE
Date: 2005
Gaussian Markov Random Fields for fusion in information form
Publisher: IEEE
Date: 05-2016
Event-driven Loop Closure In Multi-robot Mapping
Publisher: IEEE
Date: 10-2009
Guided Learning from Demonstration for Robust Transferability
Publisher: IEEE
Date: 29-05-2023
Global Localisation in Continuous Magnetic Vector Fields Using Gaussian Processes
Publisher: IEEE
Date: 04-06-2023
Continuous latent state preintegration for inertial-aided systems
Publisher: SAGE Publications
Date: 09-2023
Pulsed eddy current sensing for critical pipe condition assessment
Publisher: MDPI AG
Date: 26-09-2017
DOI: 10.3390/S17102208
Multiple defect interpretation based on Gaussian processes for MFL technology
Publisher: SPIE
Date: 11-04-2013
DOI: 10.1117/12.2009966
Towards Visual Feedback Loops for Robot-Controlled Additive Manufacturing
Publisher: Springer International Publishing
Date: 26-08-2019
Real-time sound source localisation for target tracking applications using an asynchronous microphone array
Publisher: IEEE
Date: 06-2015
Kidnapped laser-scanner for evaluation of RFEC tool
Publisher: IEEE
Date: 09-2015
Stochastic State Estimation for Simultaneous Localization and Map Building in Mobile Robotics
Publisher: Pro Literatur Verlag, Germany
Date: 07-2005
DOI: 10.5772/4651
On the observability of bearing-only SLAM
Publisher: IEEE
Date: 04-2007
Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Integrated probabilistic generative model for detecting smoke on visual images
Publisher: IEEE
Date: 05-2012
3D Lidar-IMU Calibration based on Upsampled Preintegrated Measurements for Motion Distortion Correction
Publisher: IEEE
Date: 05-2018
Constrained sampling of 2.5D probabilistic maps for augmented inference
Publisher: IEEE
Date: 10-2016
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP210101336
Start Date: 05-2021
End Date: 05-2024
Amount: $377,827.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC200100001
Start Date: 08-2021
End Date: 08-2026
Amount: $4,879,415.00
Funder: Australian Research Council
View Funded Activity