# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 5 Comparative Study for VOE
### Comparative Study of VOE Results  
The VOE results of the MLF-VO framework with backbones such as Resnet18, Resnet34, Resnet52, Resnet101, and Resnet152 on cross-datasets of the TQU-SLAM benchmark dataset are shown in Table 16. The evaluation results with a backbone of Resnet18 with \(Err_{d}\), with an error from 17.91 m to 44.45 m, with an \(RMSE\) error from 16.97 m to 49.77 m, and with an \(ATE\) error from 28.95 m to 41.64 m. The valuation results with backbone Resnet34 with \(Err_{d}\) yielded an error from 17.57 m to 52.84 m, with an RMSE error from 19.41 m to 57.61 m, and with an \(ATE\) error from 28.78 m to 38.93 m. The result in OP-D had a larger error than FD-D in all measures and backbones of the MLF-VO framework.  
Figure 10 shows the VOE results based on the MLF-VO framework compared with the GT on the evaluation subsets (Sub1, Sub2, Sub3, and Sub4). The results on the subsets (Sub1, Sub2, Sub3, and Sub4) of the MLF-VO framework have errors from 7 m to 19 m. This error result is very high and comes from the following reasons. The TQU-SLAM benchmark dataset was collected with color images, depth images, and GT data built based on calculations, measurements, and markings in the real world. While the input of the MLF-VO is only the RGB images, the depth image data were not used in the MLF-VO method. The large error of VOE is the cumulative error from the process of estimating the depth of the scene on the RGB images, the RGB images of the TQU-SLAM benchmark dataset have low-resolution, low-light images, so the process of estimating depth and VOE yielded large errors. The results show that when using the MLF-VO for the VOE, there was a very large error in the subsets (Sub1, Sub2, Sub3, and Sub4), which is based on the  
distance between the blue points (GT) and the red points (estimated pose) being very far apart, especially at the end of the FO-D. Figure 11 shows the results of the VOE based on the MLF-VO compared with the GT of visual odometry on the evaluation subsets (Sub5, Sub6, Sub7, and Sub8). The results also show a large error gap between the GT of visual odometry (blue) and the estimated visual odometry (red pose) based on the MLF-VO. This result shows that the error of VOE is still very high. Figure 12 shows the results of VOE from Sub9 to Sub12 on the TQU-SLAM benchmark dataset with MLF-VO framework with backbone Resnet18. The blue points are on the camera's GT motion trajectory, and the red points are on the camera's estimated trajectory.  
Figure 13 shows the VOE result (a) and the color image data below the depth image above of the scene during construction. Therein, (a) shows the VOE result in 3D space above and the VOE result in 2D space below. The results show that for each scene of the environment, the algorithm estimates the position of the camera in the environment from the beginning of the journey to the end of the journey. To see the visual results of VOE based on the TQU-SLAM benchmark dataset, we share the VOE result videos in 2D, 3D space, and color, depth images in the link ([[https://drive.google.com/file/d/10](https://drive.google.com/file/d/10) eJtvLo8v4 onOy0Q8 FCFjGCFyBAwa3 Kl/view?usp=sharing]([https://drive.google.com/file/d/10](https://drive.google.com/file/d/10) eJtvLo8v4 onOy0Q8 FCFjGCFyBAwa3 Kl/view?usp=sharing), accessed on 6 May 2024).  
Figure 12: VOE results on Sub9 to Sub12 of the TQU-SLAM benchmark dataset with backbone Resnet18 of MLF-VO framework.  
Figure 13: Illustration of VOE results in 3D and 2D space along with corresponding color image and depth image data of the scene. (**a**) illustrating the VOE results, (**b**) illustrating the color depth image obtained from the environment for building VOE.