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

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