Comparative analysis of localization methods for autonomous mobile robots using Robot Operating System 2
DOI:
https://doi.org/10.21439/jme.v8i.122Keywords:
Robot Operating System, Location, Mobile robots, Autonomous robotsAbstract
Localization is a fundamental requirement for mobile robots. In order to navigate autonomously and perform tasks, a robot must accurately estimate its position within the environment over time. This work aims to analyze and compare various mobile robot localization methods identified during the literature review, including Monte Carlo Localization (MCL), Adaptive Monte Carlo Localization (AMCL), Sensor Fusion, and a combined method of AMCL with Sensor Fusion, all implemented using the Robot Operating System (ROS 2). The study was carried out in the Webots simulator, with the algorithms developed in Python. Each method was evaluated in terms of efficiency (location accuracy) and performance (hardware resource consumption). The combined method of AMCL with Sensor Fusion achieved the best performance in terms of position accuracy, with a root mean squared error (RMSE) of 4–5 cm and an R² score ranging from 95.10\% to 99.79\% in Scenario 01, and from 68.20\% to 89.65\% in Scenario 02. The Sensor Fusion method ranked second, with an average error of 4–7 cm and an R² score of 94.22\% to 99.69\% in Scenario 01, and 52.27\% to 81.42\% in Scenario 02. Regarding hardware usage, Sensor Fusion showed the lowest resource consumption, using around 17\% of CPU and 32 MB of RAM, followed by AMCL, which used 21\% of CPU and 40 MB of RAM. The main contributions of this work include: the application and evaluation of different localization techniques in specific simulation scenarios, allowing for a comparative study; the use of ROS 2 and the public availability of the developed algorithms and results in a GitHub repository, supporting further studies in Webots simulation, ROS 2, and robot localization techniques.
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