Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31821
Sensor Fusion Based Discrete Kalman Filter for Outdoor Robot Navigation

Authors: Mbaitiga Zacharie


The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.

Keywords: Kalman filter, sensors fusion, robot navigation.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1955


[1] A. Pallegedara, L. Udawatta and D. Jayathilake. "Fault Tolerance Sensor Fusion Approach to Mobile Robot Navigation". Proceedings of the International Conference on Information and Automation, pp.298-303, 2005.
[2] C.J.C.H. Wathinks. "Learning from Delayed Rewards". PhD Thesis, King-s College, University of Cambridge, May 1989.
[3] T.Kohonen. " Self organizing formation of topologically correct feature maps". Biological Cybernetics, 43:59-69, 1982.
[4] Z. Mbaitiga, Adaptive Fuzzy Knowledge Based Controller for Autonomous Robot Motion Control, Journal of Computer Science Vol.6, Issue10, pp: 1048-1055, 2010, Science Publications, New York, USA.
[5] E.N. Skoundriamos and S.G Tzafesta, "Fault diagnosis vial local neural networks". Mathematics Computer Simulation, vol.60, No.2002.
[6] F. Lizarralde et al."Mobile Robot Navigation using Sensor Fusion". Proceedings of the IEEE International Conference on Robotics and Automation(Taipei), pp:1-6, 2003.
[7] Z. Mbaitiga , Security Guard Robot Detecting Human Using Gaussian Distribution Histogram Method. Journal of Computer Science Vol.6, Issue10, pp: 1144-1150, 2010, Science Publications, New York, USA.
[8] F. Lizarralde, J.Wen and L.Hsu. "Feedback stabilization of nonholonomic systems based on path space iteration". Proceedings of the 2nd International Symposium on Methods and Models in Automation and Robotics (MMAR-95),(Warsaw Poland) 1995.
[9] F. Lizarralde , J.Wen and L.Hsu. "A New Model predictive control strategy for affine nonlinear control systems ". Proceedings of American control conference,(San Diego (CA)), 1999.
[10] J.Tae-Seok,L,Jan-Myung,L.Bing Lam and S.K,Tso."A study on mult-sensor fusion for mobile robot navigation in an indoor environment" the 8th IEEE conference on mechanics and machine vision in practice, pp:455-460, 2001,Hong Kong.
[11] J.Borenstein and Y. Koren. "The vector field histogram-fast obstacle avoidance for mobile robots". IEEE transactions on robotics and automation, 7(3):278-288,1991.