Analysis on 2D Mapping for Mobile Robotonthesharped Edge Area

Simultaneous localization and mapping (SLAM) is a fundamental technique block in the indoor navigation system for most autonomous vehicles and robots. One of the issues in SLAM is that the speed of the robot may affect the mapping quality. Therefore, LiDAR self-motion distortion is a common challenge for different SLAM algorithms, especially in environments with sharp edges. Due to this issue, this study aims to analyze the impact of LiDAR self-motion distortions on three SLAM algorithms: GMapping, Hector SLAM, and Google Cartographer. These algorithms are implemented on a TurtleBot3 Burger robot to perform 2D mapping under different speed conditions (0.07m/s, 0.14m/s, and 0.22m/s) in the Control System Lab at U niversiti Malaysia Pahang AI- Sultan Abdullah (UMPSA). The quality of the generated maps is evaluated by measuring the length of predefined walls and the angle of predefined corners and comparing them with the actual dimensions in the real world. The absolute error and statistical error metrics (MAE, MSE, RMSE, and MAPE) are computed for each data point and each algorithm. The results show that Hector SLAM is the most robust algorithm under high speed, all the walls and corners can be accurately mapped, with the lowest MAPE value, due to its independence of odometry data. The results also reveal that the effect of LiDAR self-motion distortion increases with speed, as indicated by the higher error values for all the algorithms. This study contributes to the understanding of how LiDAR self-motion distortions affect the performance of different SLAM algorithms and provides insights for choosing the appropriate algorithm for different speed scenarios.