Abstract
Noise appears in images captured by real cameras. This paper studies the influence of noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM). First, an open-source synthetic dataset with different noise levels is introduced in this paper. Then the images in the dataset are denoised using the Fast and Flexible Denoising convolutional neural Network (FFDNet); the matching performances of Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) which are commonly used in feature-based SLAM are analyzed in comparison and the results show that ORB has a higher correct matching rate than that of SIFT and SURF, the denoised images have a higher correct matching rate than noisy images. Next, the Absolute Trajectory Error (ATE) of noisy and denoised sequences are evaluated on ORB-SLAM2 and the results show that the denoised sequences perform better than the noisy sequences at any noise level. Finally, the completely clean sequence in the dataset and the sequences in the KITTI dataset are denoised and compared with the original sequence through comprehensive experiments. For the clean sequence, the Root-Mean-Square Error (RMSE) of ATE after denoising has decreased by 16.75%; for KITTI sequences, 7 out of 10 sequences have lower RMSE than the original sequences. The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions.
Highlights
Simultaneous Localization and Mapping (SLAM) has been an important research direction in the field of computer vision and robotics
The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions
It can be seen that the matching rate of Oriented FAST and Rotated BRIEF (ORB) is the highest of the three, while Speeded Up Robust Features (SURF) is second and Scale Invariant Feature Transform (SIFT) is the lowest
Summary
Simultaneous Localization and Mapping (SLAM) has been an important research direction in the field of computer vision and robotics. The visual SLAM methods are based on the assumption that the image has a low noise level. How does image noise affect monocular feature-based visual SLAM, and what will happen to the accuracy and robustness after the image is denoised? An open-source dataset extended from WHU-RSVI [21] for evaluating the noise of Monocular visual SLAM systems or denoising tasks. Quantitative evaluation of the influence of different levels of noise on the feature matching of SIFT, SURF, and ORB. To evaluate the influence of noise on the visual SLAM methods, a dataset with different levels of image noise is generated.
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