A Novel Resampling Method for Particle Filter for Mobile Robot Localization

Abstract

This paper present a particle filter for mobile robot localization also known as Monte Carlo Localization (MCL) to solve the localization problem of autonomous mobile robot. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in thesimulation as well as in physical experiments.

Vision-based Monte Carlo localization for RoboCup Humanoid Kid-Size League

“…3 shows the result of stability testing in stable condition. The settling time given by the modified resampling algorithm (the blue graph) is slightly better than the result provided by the resampling algorithm proposed in [11] (the green graph). The error steady state condition is reached after the 5 th step of the algorithm steps while the other one reaches it after the 8 th step.…”

“…Final localization result will be calculated in a cluster having the highest number of samples by (11) Note that calculating the final orientation result is not straightforward because of the circularity of the angle.…”

“…Novel resampling method introduced in [11] has been proven experimentally in mobile robot using 3 ultrasonic range sensors, to solve the steady state error problem and robot kidnapping problem. The basic idea of this resampling method is that in each resampling step, a new set of particles are always drawn from the distribution, so there are no exactly the same particles.…”

“…This condition leads to an unstable localization result if we draw the 100-P% uniformly and randomly (based on [11]) along the time because sometimes they are suspected as the samples with higher weight. This condition causes the the samples converge to a wrong position (this is discussed on section IV).…”

“…[2], [3], [4], [11]) succesfully to provide the autonomous capabilities on it. They used distance sensors such as laser scanner or sonar sensors.…”