# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.2 Optical Flow Estimation
This database includes 35 clips, with 23 clips (1064 frames) used for training and 12 clips (564 frames) used for model testing. In it, the process of creating the database was carried out in three different ways: The first was \"Albedo\"; these data used the simplest pass of constant color with almost no lighting effect. The second was \"Clean\"; the data using this pass added complexity by introducing various types of lighting that make smooth gloss surfaces, self-shadowing, darkening in cavities, and darkening where the object is close to the surface. The third was \"Final\"; these data were similar to the released film and added some effects such as atmospheric effects, depth of field blur, motion blur, and color correction. Middlebury dataset [86]: Unlike other datasets, this dataset has a very small number of frames, consisting of only eight frames, and the original data were determined in the middle pair. The authors not only collected color images but also created grayscale images. The data were divided into 12 sequences for training with original data and 12 sequences for testing. Flying Chairs dataset [55]: The GT data are the model of the chair. These data include 22,872 image pairs and corresponding flow fields. Among them, 964 images were collected from Flick with the environments 'city', 'landscape', and'mountain', with a resolution of \(1024\times 768\). From this original image, the authors cropped images with dimensions \(512\times 384\) in four quadrants. Chair objects were added to the background, resulting in 809 chair types with 62 views per chair. Foggy dataset [83]: This is a synthetic dataset built by combining the defogging method with the original FlowNet2 [56], PWCNet [58], and CC [71] datasets. The defogging method was proposed by [87]. The generated data include 2346 real fog image pairs used for training, and the GT includes 100 real fog image pairs that were annotated manually. c. Evaluation measure of optical flow estimation  
To evaluate the results of the optical flow estimation, methods often use the End-Point Error (\(EPE\)) losses measure between the predicted optical flow (\(V_{e}\)) and GT (\(V_{gt}\)), as computed in the Formula (3). The unit of measurement is pixels. Based on the evaluation measure, if the \(EPE\) is small, the optical flow estimation model is better. \[EPE=\|V_{e}-V_{gt}\| \tag{3}\]  
d. Results and discussions of optical flow estimation  
The results of optical flow estimation are shown in Table 3. The results were evaluated on seven datasets when evaluated on the Sintel Clean dataset [85], and the best results were with the method of [82] (FlowNet2-IAER). On the Sintel Final dataset [85], the best results were from the method of [82] (FlowNet2-IAER). On the KITTI 2012 dataset, the best result was that of the method of [81] (sub-test-ft). On the KITTI 2015 dataset [11], the best results were from the method of [77]. On the Middlebury dataset [86], the best results were from the method of [88]. On the Flying Chairs dataset [55], the best result was that of the method of [78]. Finally, on the Foggy dataset [83], the best result was that of the method of [83]. The results show that more recent studies tend to have lower error rates. However, studies often focus on evaluating a few datasets--Sintel Clean, Sintel Final, KITTI 2012, and KITTI 2015--so there are many empty results on the remaining datasets.