Robot localization in a mapped environment using Adaptive Monte Carlo algorithm

TL;DR

This paper presents an adaptive Monte Carlo localization method for robots in known environments, utilizing probabilistic filtering and simulation tools to improve pose estimation accuracy.

Contribution

It introduces an adaptive particle filter approach for robot localization in mapped environments, implemented and tested using ROS, Gazebo, and RViz.

Findings

Effective localization in simulated environments

Improved accuracy over traditional methods

Successful navigation to goal states

Abstract

Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization.