Benchmarking and Comparing Popular Visual SLAM Algorithms
Abstract
This paper contains the performance analysis and
benchmarking of two popular visual SLAM Algorithms: RGBDSLAM
and RTABMap. The dataset used for the analysis is the
TUM RGBD Dataset from the Computer Vision Group at TUM.
The dataset selected has a large set of image sequences from a
Microsoft Kinect RGB-D sensor with highly accurate and time
synchronized ground truth poses from a motion capture system.
The test sequences selected depict a variety of problems and
camera motions faced by SLAM algorithms for the purpose of
testing the robustness of SLAM algorithms in different situations.
The evaluation metrics used for the comparison are Absolute
Trajectory Error (ATE) and Relative Pose Error (RPE). The
analysis involves comparing the Root Mean Square Error
(RMSE) of the two metrics and the processing time for each
algorithm. This paper serves as an important aid in the selection
of SLAM algorithm for different scenes and camera motions. The
analysis helps to realize the limitations of both SLAM methods.
This paper also points out some underlying flaws in the used
evaluation metrics
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