Comparison of Hector SLAM and Gmapping for a Self-driving Mobile Robot on Slippery Surface
Keywords:
mobile robot, robot position', Hector SLAM, GMapping, Relative ErrorAbstract
This work compared the performance of two Simultaneous Localisation and Mapping (SLAM) algorithms, Hector SLAM and Gmapping, for self-navigation of a mobile robot in a small, slippery surface and controlled environment. The experiment utilised the Bveeta Mini mobile robot within a tiled corridor area. The primary objective was to evaluate and compare the accuracy of these algorithms in self-navigating the robot using acquired robot positions in 2D coordinates. The experiment involved manual mapping of the environment using both Gmapping and Hector SLAM, followed by autonomous navigation tasks with each algorithm. Performance was assessed by comparing the absolute error, absolute relative error, and percentage error between the robot's position obtained from the manual map and its position during autonomous navigation provided by the SLAM algorithms. It was found that the Hector SLAM achieved higher accuracy in all navigation paths than Gmapping. Gmapping suffered from significant errors, particularly in the robot's initial position, likely due to its reliance on odometry data, which was highly susceptible to errors from the slippery surface in the experimental area. In conclusion, both algorithms can be integrated with other advanced SLAM techniques to improve the accuracy of the generated map and robot position.
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