2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy, CPU Usage, and Memory Usage

 

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Detalles Bibliográficos
Autores: Trejos Vargas, Kevin Francisco, Rincón Riveros, Laura Camila, Bolaños Torres, Miguel Eduardo, Fallas Pizarro, José Ariel, Marín Paniagua, Leonardo José
Formato: artículo original
Fecha de Publicación:2022
Descripción: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, these are 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 by using hypothesis tests besides central limit theorem.
País:Kérwá
Institución:Universidad de Costa Rica
Repositorio:Kérwá
Lenguaje:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/86591
Acceso en línea:https://hdl.handle.net/10669/86591
Palabra clave:2D SLAM
SLAM calibration
ROS
GAZEBO
Cartographer
Gmapping
HECTOR-SLAM
KARTO-SLAM
RTAB-Map
APE
Knn-Search
Plackett-Burman