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.

翻译：定位是指在地图环境中确定机器人位姿的挑战。该任务通过实现概率算法来滤除传感器测量噪声，并追踪机器人的位置与朝向。本文重点研究在已知地图环境中，利用自适应蒙特卡洛定位（亦称粒子滤波法）对机器人进行定位，并将其引导至目标状态。研究采用ROS、Gazebo与RViz作为工具进行环境仿真，并编程控制两台机器人执行定位任务。