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Dr Lakshitha Dantanarayana


Lakshitha received his PhD from the University of Technology Sydney (UTS), Australia and his B.Sc. Eng. in Electronic and Telecommunication Engineering with First Class Honours from the University of Moratuwa, Sri Lanka in 2016 and 2010 respectively.
He is currently a Postdoctoral Fellow at the Centre for Autonomous Systems at UTS where he conducts research on autonomous robot localisation and mapping.
From Nov. 2014-Jul. 2015, Lakshitha was associated with Terrestrial Robotics Engineering, and Control Lab (TREC) at the Virginia Polytechnic Institute and State University (VirginiaTech) in Blacksburg, VA, USA as a Visiting Scholar to represent UTS at the US Department of Defense sponsored DARPA Robotics Challenge 2015 which was held in California on the 05-06th June 2015.
There, he worked on developing 3D state estimation and mapping algorithms for ECHER, a full-size humanoid robot designed and developed at VirginiaTech.
Senior Software Engineer, School of Mechanical and Mechatronic Engineering
B.Sc.Eng (Hons.), Ph.D.
+61 2 9514 3143

Research Interests

Autonomous robot localisation and mapping, Robot navigation, Human robot interaction, Assistive robotics


Furukawa, T., Dantanarayana, L.I., Ziglar, J., Ranasinghe, R. & Dissanayake, G. 2015, 'Fast Global Scan Matching for High-Speed Vehicle Navigation', IEEE Xplore, Multisensor Fusion and Integration for Intelligent Systems (MFI), 2015 IEEE International Conference on, IEEE, San Diego, CA, USA, pp. 37-42.
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Dantanarayana, L., Dissanayake, G., Ranasinghe, R. & Furukawa, T. 2015, 'An extended Kalman filter for localisation in occupancy grid maps', 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS), IEEE, pp. 419-424.
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Dantanarayana, L., Ranasinghe, R., Tran, A., Liu, D. & Dissanayake, G. 2014, 'A Novel Collaboratively Designed Robot to Assist Carers', SOCIAL ROBOTICS, pp. 105-114.
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Ranasinghe, R., Dantanarayana, L., Tran, A., Lie, S., Behrens, M. & Liu, L. 2014, 'Smart Hoist: An Assistive Robot to Aid Carers', International Conference on Control Automation Robotics & Vision, Control, Automation, IEEE International Conference on Robotics and Vision (ICARCV), IEEE, Singapore, pp. 1285-1291.
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Assistive Robotics(AR) is a rapidly expanding field, implementing advanced intelligent machines which are able to work collaboratively with a range of human users; as assistants, tools and as companions. These AR devices can assist stretched carers at residential aged care facilities to safely enhance their capacity and to improve the quality of care services. The research work presented in this paper describes the pre- liminary outcomes of a design, development and implementation of a patient lifting AR device (Smart Hoist) to reduce lower back injuries to carers while transferring patients in aged care facilities. The proposed solution, a modified conventional lifter device which consists of several sensors capable of interacting with the Smart Hoist operator and its environment, and a set of powered wheels. This solution helps carers to manoeuvre the Smart Hoist safely and intuitively. Preliminary results collected from an evaluation of the Smart Hoist conducted at the premises of IRT Woonona residential care facility confirm the improved safety, comfort and confidence for the carers.
Dantanarayana, L.I., Ranasinghe, R. & Dissanayake, G. 2013, 'C-LOG: A Chamfer Distance Based Method for Localisation in Occupancy Grid-maps', IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE, Tokyo, Japan, pp. 376-381.
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In this paper, the problem of localising a robot within a known two-dimensional environment is formulated as one of minimising the Chamfer Distance between the corresponding occupancy grid map and information gathered from a sensor such as a laser range finder. It is shown that this nonlinear optimisation problem can be solved efficiently and that the resulting localisation algorithm has a number of attractive characteristics when compared with the conventional particle filter based solution for robot localisation in occupancy grids. The proposed algorithm is able to perform well even when robot odometry is unavailable, insensitive to noise models and does not critically depend on any tuning parameters. Experimental results based on a number of public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.

Journal articles

Knabe, C., Griffin, R., Burton, J., Cantor-Cooke, G., Dantanarayana, L., Day, G., Ebeling-Koning, O., Hahn, E., Hopkins, M., Neal, J., Newton, J., Nogales, C., Orekhov, V., Peterson, J., Rouleau, M., Seminatore, J., Sung, Y., Webb, J., Wittenstein, N., Ziglar, J., Leonessa, A., Lattimer, B. & Furukawa, T. 2017, 'Team VALOR's ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge', Journal of Field Robotics.
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Ryu, K., Dantanarayana, L., Furukawa, T. & Dissanayake, G. 2016, 'Grid-based Scan-to-Map Matching for Accurate 2D Map Building', Advanced Robotics, vol. 30, no. 7, pp. 431-448.
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This paper presents a grid-based scan-to-map matching technique for accurate 2D map building. At every acquisition of a new scan, the proposed technique matches the new scan to the previous scan similarly to the conventional techniques, but further corrects the error by matching the new scan to the globally defined map. In order to achieve best scan-to-map matching at each acquisition, the map is represented as a grid map with multiple normal distributions (NDs) in each cell, which is one contribution of this paper. Additionally, the new scan is also represented by NDs, developing a novel ND-to-ND matching technique. This ND-to-ND matching technique has significant potential in the enhancement of the global matching as well as the computational efficiency. Experimental results first show that the proposed technique accumulates very small errors after consecutive matchings and identifies that the scans are matched better to the map with the multi-ND representation than one ND representation. The proposed t...
Dantanarayana, L., Dissanayake, G. & Ranasinge, R. 2016, 'C-LOG: A Chamfer distance based algorithm for localisation in occupancy grid-maps', CAAI Transactions on Intelligence Technology, vol. 1, no. 3, pp. 272-284.
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