Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Authors

  • Zoltán Gyenes
    Affiliation
    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary Department of Computer Science, University of Central Florida, 4328 Scorpius St, 32816-2362 Orlando, FL, USA
  • Ladislau Bölöni
    Affiliation
    Department of Computer Science, University of Central Florida, 4328 Scorpius St, 32816-2362 Orlando, FL, USA
  • Emese Gincsainé Szádeczky-Kardoss
    Affiliation
    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
https://doi.org/10.3311/PPee.21969

Abstract

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations.

Keywords:

robotics, state perception, LiDAR sensor

Citation data from Crossref and Scopus

Published Online

2023-10-10

How to Cite

Gyenes, Z., Bölöni, L., Gincsainé Szádeczky-Kardoss, E. “Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots”, Periodica Polytechnica Electrical Engineering and Computer Science, 67(4), pp. 384–393, 2023. https://doi.org/10.3311/PPee.21969

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Articles