Diluka Moratuwage

Diluka Moratuwage

I am a lecturer at CQUniversity, based in Gladstone Marina Campus, Queensland, Australia.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering

Published in IEEE Transactions on Signal Processing, 2023

This paper presents a linear complexity Gibbs sampling approach for GLMB filtering, significantly reducing the computational burden of multi-object state estimation.

Recommended citation: C. Shim, B.T. Vo, B.N. Vo, J. Ong, and D. Moratuwage (2023). "Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering." IEEE Transactions on Signal Processing, 71, 1981-1994.
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Multi-Object Tracking with an Adaptive Generalized Labelled Multi-Bernoulli Filter

Published in Elsevier Signal Processing, 2022

This paper proposes an adaptive GLMB filter for multi-object tracking, improving adaptability across varying clutter and detection scenarios.

Recommended citation: C.T. Do, T.T.D. Nguyen, D. Moratuwage, C. Shim and Y.D. Chung (2022). "Multi-Object Tracking with an adaptive generalized labelled multi-Bernoulli filter." Elsevier Signal Processing, 196.

Audio-Visual Based Online Multi-Source Separation

Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022

This paper presents an audio-visual approach to online multi-source separation, combining visual and audio cues for improved performance.

Recommended citation: J. Ong, B.T. Vo, S. Nordholm, B.N. Vo, D. Moratuwage and C. Shim (2022). "Audio-Visual Based Online Multi-Source Separation." IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 1219-1234.

Multi-Scan Multi-Sensor Multi-Object State Estimation

Published in IEEE Transactions on Signal Processing, 2022

This paper addresses multi-object state estimation using multiple sensors and multiple scans, leveraging the labeled multi-Bernoulli framework.

Recommended citation: D. Moratuwage, B.N. Vo, B.T. Vo and C. Shim (2022). "Multi-Scan Multi-Sensor Multi-Object State Estimation." IEEE Transactions on Signal Processing, 70, 5429-5442.
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Delta-Generalized Labelled Multi-Bernoulli SLAM with Optimal Kernel-Based Particle Filtering

Published in Sensors, 2019

This paper presents a delta-GLMB approach to SLAM incorporating optimal kernel-based particle filtering for improved trajectory and map estimation.

Recommended citation: D. Moratuwage, M. Adams, F. Inostroza (2019). "Delta-Generalized Labelled Multi-Bernoulli Simultaneous Localization and Mapping with the Optimal Kernel based Particle Filtering Approach." Sensors, 19(10).
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RFS Collaborative SLAM: SLAM in Dynamic High-Clutter Environments

Published in IEEE Robotics and Automation Magazine, 2014

This paper presents a random finite set based collaborative SLAM framework for dynamic, high-clutter environments using multiple vehicles.

Recommended citation: D. Moratuwage, D. Wang, R. Akshay, P. Senarathne, H. Wang (2014). "RFS Collaborative SLAM: SLAM in Dynamic High-Clutter Environments." IEEE Robotics and Automation Magazine, 21(2), 53-59.

Conference Papers

Generalized Label Grouping for Scalable Trajectory Estimation

Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022

A generalized label grouping strategy to improve scalability of trajectory estimation in multi-object tracking.

Recommended citation: C. Shim, J.Y. Lee, D. Moratuwage, D.Y. Kim, Y.D. Chung (2022). "Generalized Label Grouping for Scalable Trajectory Estimation." ICCAIS, Hanoi, Vietnam.

A Faster Implementation of Multi-sensor Generalized Labeled Multi-Bernoulli Filter

Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022

A computationally efficient implementation of the multi-sensor GLMB filter for scalable multi-object tracking.

Recommended citation: D. Moratuwage, Y. Punchihewa, J.Y. Lee (2022). "A Faster Implementation of Multi-sensor Generalized Labeled Multi-Bernoulli Filter." ICCAIS, Hanoi, Vietnam.

A Moving Window Based Approach to Multi-scan Multi-target Tracking

Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022

A moving window approach to multi-scan multi-target tracking for improved computational efficiency in real-time applications.

Recommended citation: D. Moratuwage, C. Shim, and Y. Punchihewa (2022). "A Moving Window Based Approach to Multi-scan Multi-target Tracking." ICCAIS, Hanoi, Vietnam.

Space Object Tracking using a Jump Markov System based Delta-GLMB Filter for Space Situational Awareness

Published in Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference, Maui, Hawaii, 2019

Application of a Jump Markov System based delta-GLMB filter to space object tracking for space situational awareness.

Recommended citation: D. Moratuwage, M. Adams, L. Cament (2019). "Space Object Tracking using a Jump Markov System based Delta-GLMB filter for Space Situational Awareness." AMOS Conference, Maui, Hawaii.

Delta-Generalized Labelled Multi-Bernoulli Simultaneous Localization and Mapping

Published in International Conference on Control, Automation and Information Sciences (ICCAIS), Hangzhou, China, 2018

Conference paper introducing the delta-GLMB SLAM framework for simultaneous localisation and mapping.

Recommended citation: D. Moratuwage, M. Adams, F. Inostroza (2018). "Delta-Generalized Labelled Multi-Bernoulli Simultaneous Localization and Mapping." ICCAIS, Hangzhou, China.

3D Feature Points Detection on Sparse and Non-Uniform Pointcloud for SLAM

Published in International Conference on Advanced Robotics (ICAR), Hong Kong, China, 2017

A method for detecting 3D feature points on sparse and non-uniform point clouds for robust feature-based SLAM.

Recommended citation: P. Siritanawan, D. Moratuwage, D. Wang (2017). "3D feature points detection on sparse and non-uniform pointcloud for SLAM." ICAR, Hong Kong, China.

A Hybrid Probabilistic and Point Set Registration Approach for Fusion of 3D Occupancy Grid Maps

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016

A hybrid approach combining probabilistic methods and point set registration for fusing 3D occupancy grid maps in multi-robot mapping.

Recommended citation: Y. Yue, D. Wang, P.G.C.N. Senarathne, D. Moratuwage (2016). "A hybrid probabilistic and point set registration approach for fusion of 3D occupancy grid maps." IEEE SMC.

Collaborative Multi-Vehicle SLAM with Moving Object Tracking

Published in IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013

A collaborative SLAM framework for multiple vehicles that simultaneously tracks moving objects using random finite set theory.

Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2013). "Collaborative Multi-Vehicle SLAM with Moving Object Tracking." IEEE ICRA, Karlsruhe, Germany.

Extending Bayesian RFS SLAM to Multi-Vehicle SLAM

Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China, 2012

Extension of the Bayesian random finite set SLAM framework to the multi-vehicle setting for collaborative localisation and mapping.

Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2012). "Extending Bayesian RFS SLAM to Multi-Vehicle SLAM." IEEE ICARCV, Guangzhou, China.

A Hierarchical Approach to the Multi-Vehicle SLAM Problem

Published in Information Fusion (FUSION), Singapore, 2012

A hierarchical approach to solving the multi-vehicle SLAM problem, improving scalability and consistency across multiple agents.

Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2012). "A Hierarchical Approach to the Multi-Vehicle SLAM problem." FUSION, Singapore.

Towards Fully Autonomous Vision-Based Vehicle Navigation in Outdoor Environments

Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2010

Progress towards a fully autonomous vision-based navigation system for vehicles in unstructured outdoor environments.

Recommended citation: P. Moghadam, W.S. Wijesoma, M.D.P. Moratuwage (2010). "Towards fully autonomous vision-based vehicle navigation system in outdoor environments." IEEE ICARCV, Singapore.

Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments

Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2010

Collaborative localisation and mapping in high clutter density environments using multiple vehicles.

Recommended citation: M.D.P. Moratuwage, W.S. Wijesoma, B. Kalyan, N.M. Patrikalakis, P. Moghadam (2010). "Collaborative multi-vehicle localization and mapping in high clutter environments." IEEE ICARCV, Singapore.

A Random Finite Set Based Detection and Tracking using 3D LIDAR in Dynamic Environments

Published in IEEE International Conference on Systems Man and Cybernetics (SMC), Turkey, 2010

Application of random finite set theory to detection and tracking using 3D LIDAR sensors in dynamic environments.

Recommended citation: B. Kalyan, K.W. Lee, S. Wijesoma, D. Moratuwage, N.M. Patrikalakis (2010). "A random finite set based detection and tracking using 3D LIDAR in dynamic environments." IEEE SMC, Turkey.

MarineSIM: Robot Simulation for Marine Environments

Published in IEEE OCEANS, Sydney, Australia, 2010

MarineSIM, a robot simulation environment designed for marine robotics research and development.

Recommended citation: P.G.C.N. Senarathne, W.S. Wijesoma, K.W. Lee, B. Kalyan, M.D.P. Moratuwage (2010). "MarineSIM: Robot Simulation for Marine Environments." IEEE OCEANS, Sydney, Australia.

Collaborative Multi-Vehicle Localization and Mapping in Marine Environments

Published in IEEE OCEANS, Sydney, Australia, 2010

A collaborative localisation and mapping framework for multiple vehicles operating in marine environments.

Recommended citation: M.D.P. Moratuwage, W.S. Wijesoma, B. Kalyan, J.F. Dong, P.G.C.N. Senarathne, F.S. Hover, N.M. Patrikalakis (2010). "Collaborative multi-vehicle localization and mapping in marine environments." IEEE OCEANS, Sydney, Australia.