Open Access Open Access  Restricted Access Subscription or Fee Access

An Extended Kalman Filter-Based Simultaneous Localization and Mapping Algorithm for Omnidirectional Indoor Mobile Robot

(*) Corresponding author

Authors' affiliations



Simultaneous Localization And Mapping (SLAM) is the ability of an autonomous robot to localize itself within the surroundings, while also constructing a map. It has several applications in a variety of industries, especially in indoor navigation, Autonomous driving, and robotics. This research uses an omnidirectional mobile robot to present the SLAM algorithm based on Extended Kalman Filter (EKF) in an indoor environment. Usually, SLAM combines data from odometry and other sensors, in order to overcome the accumulating error due to the wheel sliding that usually appears when using the odometry alone. Thus, in this study a Kinect sensor is used along with an odometer sensor in an unknown environment to reach a predefined point with a free collusion path using three main fuzzy controllers as part of its navigation system. The first fuzzy controller is responsible for the goal-seeking problem, where the robot moves towards the goal, the second and third controllers are responsible for static and dynamic obstacles avoidance. The robot changes its direction to avoid the static obstacles and its direction and speed to avoid the dynamic obstacles. An EV3 Lego robot is used to verify the effectiveness of the proposed study. Results shows the effectiveness of the used algorithm; the decentralized algorithm simplifies the control process, which decreases the error with the help of EKF.
Copyright © 2023 Praise Worthy Prize - All rights reserved.


SLAM; EKF; Kinect; Fuzzy; Navigation; Omnidirectional Wheel; Obstacle Avoidance

Full Text:



Tang, M., Chen, Z. & Yin, F. SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter. Int. J. Control Autom. Syst. 20, 1327-1335 (2022).

Do, C.H., Lin, H.Y.: Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping. Int. J. Adv. Rob. Syst. 16(5), 172988141987464 (2019).

Hammia, S., Hatim, A., Bouaaddi, A., Najoui, M., Jakjoud, F., Ez-ziymi, S., Efficient EKF-SLAM's Jacobian Matrices Hardware Architecture and its FPGA Implementation, (2021) International Review of Electrical Engineering (IREE), 16 (5), pp. 484-496.

D. Talwar and S. Jung, Particle Filter-based Localization of a Mobile Robot by Using a Single Lidar Sensor under SLAM in ROS Environment, 2019 19th International Conference on Control, Automation and Systems (ICCAS), 2019, pp. 1112-1115.

M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, Fast SLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping, Proceedings of IJCAI, 2003.

J. J. Leonard and H. F. Durrant-Whyte, Mobile robot localization by tracking geometric beacons, Robotics and Automation, IEEE Transactions, vol. 7, pp. 376-382, 1991.

M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem, In Eighteenth national conference on Artificial intelligence, Menlo Park, CA, USA, 2002, pp. 593-598.

Esparza-Jiménez, J.O.; Devy, M.; Gordillo, J.L. Visual EKF-SLAM from Heterogeneous Landmarks. Sensors 2016, 16, 489.

Pei, F-J, Wu, M., Zhang, S.: Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization, The Scientific World Journal, 86, Paper, (2014).

Karam, S.; Lehtola, V.; Vosselman, G. Strategies to Integrate IMU and LIDAR SLAM for Indoor Mapping. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, V-1-2020, 223-230.

Karam, S.; Nex, F.; Chidura, B.T.; Kerle, N. Microdrone-Based Indoor Mapping with Graph SLAM. Drones 2022, 6, 352.

Kim, H.; Kim, D.; Kim, S. Real-time Geospatial Positioning for UAVs in GPS-Denied Environment Using LiDAR Data. In Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA, 6-10 January 2020; p. 2194.

El Farnane, A., Youssefi, M., Mouhsen, A., Kachmar, M., Oumouh, A., El Aissaoui, A., Trajectory Tracking of Autonomous Driving Tricycle Robot with Fuzzy Control, (2022) International Review of Automatic Control (IREACO), 15 (2), pp. 80-86.

Batayneh, W., Bataineh, A., Jaradat, M., Intelligent Adaptive Fuzzy Logic Genetic Algorithm Controller for Anti-Lock Braking System, (2021) International Review on Modelling and Simulations (IREMOS), 14 (1), pp. 44-54.

Bataineh, A., Batayneh, W., Okour, M., Intelligent Control Strategies for Three Degree of Freedom Active Suspension System, (2021) International Review of Automatic Control (IREACO), 14 (1), pp. 17-27.

Batayneh, W.; AbuRmaileh, Y. Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers. Sensors 2020, 20, 3525.

Batayneh, W., Aburmaileh, Y., Bataineh, A., Experimental Implementation of Tracking Error Elimination for Omnidirectional Wheelchair Using PD-Fuzzy-P Controller, (2021) International Review of Automatic Control (IREACO), 14 (2), pp. 102-112.

N. V. Belov, D. P. Airapetov, B. Y. Buyanov and V. A. Verba, SLAM Implementation For Mobile Robots Using Physical Sensors, Systems of Signals Generating and Processing in the Field of on Board Communications, 2019, pp. 1-6.

M. Kulkarni, P. Junare, M. Deshmukh and P. P. Rege, Visual SLAM Combined with Object Detection for Autonomous Indoor Navigation Using Kinect V2 and ROS, 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 2021, pp. 478-482.

Ganiev, A., & Lee, K. H. (2018). A study of autonomous navigation of a robot model based on SLAM, ROS, and kinect. International Journal of Engineering and Technology (UAE), 7(3.33), 28-32.

Cho, H.; Yeon, S.; Choi, H.; Doh, N. Detection and Compensation of Degeneracy Cases for IMU-Kinect Integrated Continuous SLAM with Plane Features. Sensors 2018, 18, 935.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize