Evaluation of the depth camera based SLAM algorithms

Transcription

1 Electri c Machines and Control Vol. 21 No. 12 Dec SLAM : 3 ( SLAM), SLAM V2,RTAB-Map DVO SLAM, 3 SLAM SLAM, TUM ICL-NUIM, SLAM, SLAM, SLAM :, SLAM V2;, DVO SLAM;, RTAB-Map : ; ; ; ; DOI: /j. emc TP 13 A X Evaluation of the depth camera based SLAM algorithms MAN Chun-tao 1 CAO Miao 1 2 LI Wei 1 1. School of Automation Harbin University of Science and Technology Harbin China 2. Key Laboratory of Complex System and Intelligence Science Institute of Automation China Academy of Sciences Beijing China Abstract Three typical depth camera based simultaneous localization and mapping SLAM algorithms including SLAM V2 RTAB-Map and DVO SLAM whose theories and features were introduced. By using two open-source SLAM datasets including TUM dataset and ICL-NUIM dataset the above three SLAM algorithms were evaluated and the index included the accuracy performance and robustness of the SLAM algorithms. The results of the experiments demonstrate that SLAM V2 is chosen when accuracy and robustness are prior to speed DVO SLAM is chosen when speed and robustness are prior to accuracy RTAB-Map is chosen when speed and accuracy are prior to robustness. Keywords SLAM algorithm algorithm evaluation visual odometry mapping depth camera : ( F ) : ( 1965 ),,,, ; : ( 1992 ),,, ; ( 1982 ),,,,

2 12 SLAM SLAM SLAM V2 1-2 RTAB-Map DVO SLAM 3 Kinect Xtion Pro Live 1. 1 SLAM V2 SLAM SLAMV2 SLAM Daniel 1 SLAM SLAM 3 SLAM SLAM SLAM RGB- SLAM D SLAM SLAM 4-7 SLAM SLAM SLAM SLAM SLAM Sturm 8 SLAM TUM 1 SLAM V2 Mur-Artal 9 Fig. 1 Schematic overview of SLAM V2 SLAM 3 Yousif SLAM SLAM SLAM Kinect SLAM Endres 11 SLAM TUM SLAM OpenCV 12 SLAM SLAM SLAM 3 SLAM 13 SLAM V SLAM RTAB-Map DVO SLAM SLAM TUM ICL-NUIM SLAM SLAM g 2 o 14

3 62 21 SLAM ground truth ground truth SLAM SLAM SLAM ICL-NUIM OctoMap 15 TUM 1 SLAM 1. 2 RTAB-Map 2 RTAB-Map SLAM ICL-NUIM SLAM TUM Michaud 16 3 SURF 2. 2 t t ground truth SLAM F = Q -1 SP Q ground truth P 1. 3 DVO SLAM DVO SLAM Horn SLAM 18 Christian 19 P Q S 1 2 t SLAM ground truth F t 3 g 2 o 2 SLAM V2 RTAB-Map DVO SLAM 3 SLAM TUM 20 ICL-NUIM SLAM V2 RT- 21 SLAM AB-Map ICL-NUIM DVO SLAM 1 Intel Core i7 SLAM V2 RTAB-Map GHz 16GB SLAM V RTAB-Map 2 DVO SLAM ground truth F max m 1 SLAM V2 RTAB-Map 2 TUM DVO SLAM SLAM SLAM V2 RTAB-Map TUM xyz SLAM

4 12 SLAM 63 RTAB-Map DVO SLAM DVO SLAM RGB- 1 SLAM TUM D SLAM Table 1 Accuracy of the SLAM algorithm with SLAM V2 respect to the TUM dataset SLAM 1 RGB- D SLAM SLAM SLAM t SLAM t SLAM t - t SLAM Table 2 Accuracy of the SLAM algorithm with 3 4 s respect to the ICL-NUIM dataset 1 TUM DVO SLAM SLAM V2 SLAM V2 RTAB-Map ICL-NUIM DVO SLAM SLAM V2 RTAB-Map fr1_ fr1_desk fr1_floor fr1_plant fr1_room fr1_teddy SLAM ICL-NUIM room room room room RTAB-Map Fig. 2 Robustness of the RTAB-Map algorithm SLAM s SLAM 1 TUM DVO SLAM SLAM V2 SLAM RTAB-Map ICL-NUIM SLAM V2 RTAB-Map DVO SLAM SLAM SLAM

5 64 21 SLAM SLAM Table 3 SLAM ICL-NUIM Runtime of the SLAM algorithm with respect to the ICL-NUIM dataset room room room room Table 4 SLAM TUM Runtime of the SLAM algorithm with respect to the TUM dataset fr1_ fr1_desk fr1_floor fr1_plant fr1_room fr1_teddy Map fr1_360 fr1_desk2 fr1_room fr1_teddy room0 room3 2 a TUM fr1_desk2 SLAM 2 b 2 c 2 d 1 SLAM TUM fr1 _360 fr1 _room fr1 _teddy 2 e 2 f SLAM V2 2 SLAM 3 RTAB-Map 2 RTAB- SLAM ICL-NUIM room0 room3 SLAM SLAM 2 RTAB- RTAB-Map Map 3 SLAM SLAM RTAB-Map DVO SLAM SLAM 1 RGB- D SLAM V2 DVO SLAM 100% 2 RTAB-Map 6 10 SLAM V2 DVO SLAM RTAB-Map Table 5 5 SLAM Robustness of the SLAM algorithm TUM 6 /6 3 /6 6 /6 ICL-NUIM 4 /4 3 /4 4 /4 3 SLAM 1 SLAM V2 SIFT 22 2 DVO SLAM SLAM DVO SLAM SLAM RTAB-Map SLAM SLAM SLAM

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