Simultaneous localization and mapping embedded with particle filter algorithm

Abstract

In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated with particle filtering and a weighted averaging algorithm respectively, and are linked through an additional feedback and correction process. An adaptive re-sampling method is used to reduce the computational load. The results of the simulation indicate that the PF-SLAM algorithm can localize the robot and landmarks accurately, and the error of landmarks' estimation converges better than general SLAM algorithms.

Review: Issues and Challenges of Simultaneous Localization and Mapping (SLAM) Technology in Autonomous Robot

“…Control logic: This is the central processing unit of the robot, which receives input from the sensors and uses it to make decisions about how the actuators should be activated. This can be implemented in a variety of ways, such as with a microcontroller, a computer, or a dedicated hardware device [15]. Particle Filtering estimates the robot's posture using sensor data by: • Estimating its surrounding location • Adjusting its position at each time step using landmark data collected by sensing devices.…”