- Title
- Comparative study on object tracking algorithms for mobile robot navigation in GPS-denied environment
- Creator
- Hewawasam, Hasitha; Ibrahim, Yousef; Kahandawa, Gayan; Choudhury, Tanveer
- Date
- 2019
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/169757
- Identifier
- vital:14074
- Identifier
-
https://doi.org/10.1109/ICIT.2019.8754960
- Identifier
- ISBN:9781538663769
- Abstract
- This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.; Proceedings of the IEEE International Conference on Industrial Technology
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 19-26
- Rights
- Copyright © 2019 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Autoregressive model; GPS-denied environment; Kalman filter; Mobile robot navigation; Object tracking; Particle filter; Physical based tracking; Point based tracking; Statistical based tracking
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