# 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
#### 3.1.4 DL Modules Add to the Visual SLAM Algorithm
To extract deep features, the proposed method uses a VGG16 architecture and uses five convolution layers, four max-pooling layers, and two dense layers. As a result, the proposed method has eight times more loop closure detection accuracy than traditional features. [PERSON] et al. [168] proposed a triple loss-based metric learning method to embed into the Visual SLAM system to increase the accuracy of closed-loop detection. This method converted keyframes into feature vectors, evaluating the similarity of keyframes by calculating the Euclidean distance of feature vectors. Features on keyframes are extracted using ResNet_V1_50, with an average-pooling output size of \(2048\times 1\times 1\), and using a fully connected layer (2048-1024-128). [PERSON] et al. [169] proposed a deep-feature-matching-based keyframe retrieval method to perform loop closure detection in the Visual SLAM semantic system called deep feature matching (DFM); this method is based on the CNN method. Involves matching of the implementation method's current scenes with the recorded keyframes and finding the transformation between the matched keyframes for trajectory correction by matching the local pose graphs. This method converted keyframe descriptors and pose graphs into a sparse image, with each keyframe into a feature point. City Centre [170] was collected on a road near the city center with many moving objects such as people and vehicles in environmental conditions with a lot of sun and wind, causing the tree shadows to change a lot. Data were collected on a road with a total length of 2 km, and 2474 images were collected, with each data collection point marked in yellow and any two images collected at the same location marked in red and connected by one line. Gardens Point Walking (GPW) [163] was collected while traveling three times on a road at the QUT campus in Brisbane, Australia. This dataset shows large differences in view direction, dynamic objects, occlusions, and the illumination of each pass through this path. Of the three walks on this road, two were done in one day with one walking on the left-hand side and one time on the right-hand side of pedestrians. Therein, the i-th image in this sequence is matched with any i-th image in the other two sequences. To evaluate the performance of the loop closure detection module DL in the Visual SLAM system, the researchers used several datasets with RGB-D data, as shown below. The Area Under the Curve (_AUC_) is an aggregate measure of the performance of a binary classifier across all possible threshold values. The ROC curve (the receiver operating characteristic curve) is a curve that represents the classification performance of a classi  
fication model at thresholds. Essentially, it defines the True Positive Rate (\(TPR\)) vs False Positive Rate (\(FPR\)) for different threshold values. The \(TPR\) and \(FPR\) values are calculated as follows:  
\[TPR=\frac{TP}{TP+FP};FPR=\frac{FP}{TN+FN} \tag{12}\]  
The AUC is an index calculated based on the ROC curve to evaluate how well the model can classify. The area under the ROC curve and on the horizontal axis is the \(AUC\), with a value in the range [0, 1]. The \(RMSE\) has been presented above. The mean of the trajectory error (\(MTE\)) is defined in Formula (6). The Average (Avg.) Good Match (%) is the pose graphs' good matches (inliers) rate. The results of Visual SLAM when using the DL loop closure detection module are presented in Table 9. The studies were evaluated on the KITTI 2012, City Centre [170], GPW [163], and TUM RGB-D SLAM datasets with the \(AUC\), \(MTE\), \(RMSE\), and Avg. Good Match measures. With measures \(AUC\) and Avg. Good Match, the better the results are, with measures \(MTE\) and \(RMSE\), the smaller the results are. f. Others module DL  
Camera relocalization is the process of estimating the camera's location and orientation in the data collection environment using images of the captured environment as input. To evaluate the performance of this module, studies often evaluate based on two metrics: angular (Ang.)