2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage
Kevin Trejos,
Laura Rincón,
Miguel Bolaños,
José Fallas,
Leonardo Marín
Affiliations
Kevin Trejos
Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa Rica
Laura Rincón
Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa Rica
Miguel Bolaños
Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa Rica
José Fallas
Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa Rica
Leonardo Marín
Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa Rica
The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett–Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.
