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Indoor Robot Localisation using Kalman Filter


  • Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University – 641112, India


Objective of this paper is to reduce the error in the estimation of the position of the robot. The localization of robot is a critical aspect in making them completely autonomous. Hence accuracy in estimating the position of the robot is of paramount importance. In order to make decisions without direct manual instructions the robot has to be aware of its position in the environment along with the situation at hand. For this effect the robots are built with sensors specifically designed to suit the need of the environment to gain feedback from the surrounding thereby obtaining data about its placement and the conditions around it. It is here that the problem of accuracy arises as there are uncertainties in both the feedback system as well as controlling system of the robot. To make a logical decision, the errors have to be minimized by optimally combining the data received from all the sources. The estimation theory provides us with a solution in the form of Kalman filter algorithm. It is an adaptive filter that combines uncertain data to obtain valid values of output required. It is seen that as SNR increases MSE in the position of the robot decreases thus accuracy increases. In this paper we concentrate on the issue of robot localization in closed space whose dimensions are previously known. We model the localization process as a linear phenomenon, as Kalman filter algorithm can only be used for linear systems. Using MATLAB Simulink we simulate our model to verify the concepts and validate the use of this filter in accurately determining the position of robot.


Adaptive Filter, In-door Navigation, Indoor Robots Kalman Filter, Robot Localization.

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  • Robot localization and Kalman filters-on finding your position in a noisy world. Date Accessed: 1/09/2003.
  • Kalman Filtering implementation with matlab. Date Accessed: 2004 Nov.
  • Sim R. Mobile robot localisation using learned landmarks. Cite SeerX. 1998 Jul. p. 1–85.
  • Beardsley PA, Reid ID, Zisserman A, Murray DW. Active visual navigation using non-metric structure. Computer Vision. 1995. Proceedings, Fifth International Conference on 1995 Jun. p. 58–65.
  • Huang G, Rad A, Wong Y, Ip Y. SLAM with MTT: theory and initial results. Robotics, Automation and Mechatronics. 2004 IEEE Conference on 2004 Dec.2. p. 834–9.
  • Minato T, Ishiguro H. Distributed vision system for robot localisation in indoor environment. Cite SeerX. 2000. p. 1–6.
  • Cumani A, Guidicci A. Mobile robot localisation with stereo vision. Proceedings of the 5th WSEAS International Conference on Signal Processing. Computational Geometry and Artificial Vision, Malta. 2005 Sep. p. 176–89.
  • Thompson S, Kagami S. Humanoid robot localisation using stereo vision. 5th IEEE-RAS International Conference on Humanoid Robots. 2005 Dec. p. 19–25.
  • Inaki Rano, Elena Lazkano, Basilio Sierra. On the application of colour histograms for mobile robot localisation. Cite SeerX. 2005. p. 1–6.
  • Sabatini A, Di-Benedetto O. Towards a robust methodology for mobile robot localisation using sonar. Robotics and Automation. 1994. Proceedings, 1994 IEEE International Conference on 1994 May.4. p. 3142–7.
  • Ashokaraj I, Tsourdos A, White B, Silson P. Robot Localisation using Interval Analysis. Sensors, 2003. Proceedings of IEEE. 2003 Oct.1. p. 30–35.
  • Dias F, Sch¨afer H, Natal L, Cardeira C. Mobile robot localisation for indoor environments based on ceiling pattern recognition. 2015 IEEE International Conference on Autonomous Robot Systems and Competitions. 2015. p. 65–70.
  • Kalman and extended kalman filters: concept, derivation and properties. Date Accessed: 2004/02.
  • 2-D tracking of objects using kalman filter. Date Accessed: 3/02/2011.
  • Kwok N, Kwong S. Path planning for mobile robot localisation and mapping. Industrial Electronics Society. 2004. IECON 2004. 30th Annual Conference of IEEE. 2004 Nov.1. p. 603–8.
  • Vijaya A, Koteswara Rao S, Jawahar A, Karishma SB. Application of parameterized modified gain bearings-only extended kalman filter for undersea tracking. Indian Journal of Science and Technology. 2016Apr; 9(13): 1–6.


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