Robot localization in a mapped environment using Adaptive Monte Carlo algorithm
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929654873456640 |
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| author | Das, Sagarnil |
| author_facet | Das, Sagarnil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robot localization in a mapped environment using Adaptive Monte Carlo algorithm Das, Sagarnil Robotics 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. |
| title | Robot localization in a mapped environment using Adaptive Monte Carlo algorithm |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.01153 |