用自适应蒙特卡洛算法实现机器人在已知环境中的精准定位
Robot localization in a mapped environment using Adaptive Monte Carlo algorithm
- 采用自适应粒子滤波算法融合传感器数据
- 在仿真环境中实现机器人位置与方向的稳定跟踪
- 适合对机器人导航算法感兴趣的开发者
定位是确定机器人在已知环境中的位姿（位置与朝向）的挑战。本文通过概率算法过滤噪声传感器数据，实现机器人在已知地图环境中的定位，并引导其到达目标状态。实验基于ROS、Gazebo和RViz平台，搭建仿真环境并编程两个机器人完成定位任务，验证了自适应蒙特卡洛定位（Adaptive Monte Carlo Localization）方法的有效性。
原文摘要 · Abstract (English)
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.
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