2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage
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 ch...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/18/6903 |
| _version_ | 1797482561397587968 |
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| author | Kevin Trejos Laura Rincón Miguel Bolaños José Fallas Leonardo Marín |
| author_facet | Kevin Trejos Laura Rincón Miguel Bolaños José Fallas Leonardo Marín |
| author_sort | Kevin Trejos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:35:10Z |
| format | Article |
| id | doaj.art-c6d6be0fc3a54b4588c9c977f21ab4c2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:35:10Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c6d6be0fc3a54b4588c9c977f21ab4c22023-11-23T18:51:09ZengMDPI AGSensors1424-82202022-09-012218690310.3390/s221869032D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory UsageKevin Trejos0Laura Rincón1Miguel Bolaños2José Fallas3Leonardo Marín4Control Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa RicaControl Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa RicaControl Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa RicaControl Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa RicaControl Engineering Research Laboratory (CERLab), Electrical Engineering School, Engineering Faculty, University of Costa Rica (UCR), San Pedro, San José 11501-2060, Costa RicaThe 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.https://www.mdpi.com/1424-8220/22/18/69032D SLAMSLAM calibrationROSGAZEBOCartographerGmapping |
| spellingShingle | Kevin Trejos Laura Rincón Miguel Bolaños José Fallas Leonardo Marín 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage Sensors 2D SLAM SLAM calibration ROS GAZEBO Cartographer Gmapping |
| title | 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage |
| title_full | 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage |
| title_fullStr | 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage |
| title_full_unstemmed | 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage |
| title_short | 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage |
| title_sort | 2d slam algorithms characterization calibration and comparison considering pose error map accuracy as well as cpu and memory usage |
| topic | 2D SLAM SLAM calibration ROS GAZEBO Cartographer Gmapping |
| url | https://www.mdpi.com/1424-8220/22/18/6903 |
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