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
Abstract
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.