Introducing the Particle Filter Method in Determination of the Robot Initial Position in Premises

Authors: Bobkov A.V., Alkhatib M.N. Published: 02.04.2024
Published in issue: #1(146)/2024  

Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing  
Keywords: robot position determination, Hough space, particle filter, autonomous navigation, logistics robot


The paper considers the problem of determining the robot own position from the lidar data using a modified particle filter. The problem is relevant for many practical applications related to development and operation of the autonomous mobile platforms, for example, logistics robots in automated production and warehousing. The particle filter method most used in solving this problem is considered. The method involves generating the virtual particles that represent the robot probable position, changing the particles state according to the odometry data and subsequent filtering in accordance with matching in the observed and estimated lidar data. A drawback of the method was identified, i.e., high initial number of particles required in the method rapid convergence. The paper proposes a modified scheme based on preliminary detection of the large scene elements in the lidar data. It suggests introducing data transformation into Hough space for this purpose making it possible to identify the largest straight segments in the lidar data and correlate them with the premises map. Mathematical simulation was carried out in the MATLAB, ROS, and Gazebo environments to study the proposed algorithm properties. Experiments showed that the modified method could significantly reduce the particles initial number and make it possible to work in real time even in the large premises

Please cite this article in English as:

Bobkov A.V., Alkhatib M.N. Introducing the particle filter method in determination of the robot initial position in premises. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2024, no. 1 (146), pp. 74--92 (in Russ.). EDN: CTDPBE


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