# 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
#### 3.1.1 Depth Estimation
The dataset provides annotations for object detection, tracking, and segmentation, as well as depth maps and calibration parameters. The KITTI dataset is widely used to train and evaluate DL models for automated driving and robotics. KITTI dataset is collected from two high-resolution camera systems: a Velodyne HDL-64E laser scanner (grayscale and color) and a state-of-the-art OXTS RT 3003 localization system (a combination of devices such as GPS, GLONASS, security IMU, and RTK correction signals). These devices were mounted on a car and collected data over a distance of 39.2 km. The resolution of the image produced is \(1240\times 376\) pixels. The GT data for evaluating Visual SLAM models and VO include 3D pose annotation data of the scene. The GT data to evaluate object detection models and 3D orientation estimation include accurate 3D bounding boxes for object classes. The 3D object's point cloud data are marked through manual labeling. In the improved dataset of the KITTI dataset, [PERSON] et al. [11] developed additional data to evaluate the optical flow algorithm. The authors used the 3D CAD model in the Google 3D Warehouse database to build 3D scenes with static elements and insert moving objects. The NYUDepth dataset was proposed by [49]. The authors used Microsoft (MS) Kinect to collect data from three US cities with 464 different indoor scenes and classified them into 26 scene classes with a total of 1449 RGB-D images/3D scenes. Within the scenes, there are 35,064 distinct objects spread across 894 different classes and labeled manually. The Make3D dataset was proposed by [50]. This database includes 534 pairs of RGB-D images and color images, with a resolution of \(2272\times 1704\) pixels, and depth images with a resolution of \(55\times 305\) pixels. The training data include 400 images, and the testing data include 134 images collected from a 3D scanner. In addition, this database was also supplemented with 588 image pairs from the Internet by someone not part of the data collection project with a size of 800 \(\times\) 600 pixels. The Cityscapes dataset was proposed by [51]. This dataset was collected from stereo cameras using 1/3 in CMOS 2 MP sensors (OnSemi AR0331) in 50 different cities of outdoor environments. It has been used to evaluate object detection and classification models, especially for the 3D scene. The 3D scene is a 3D scene with 1449 RGB-D images and 3D scenes with 1449 RGB-D images. cially DL models. The GT data were prepared with 5000 manually annotated images from 27 cities in a dense pixel-level method. In addition, it is also supplemented with 20,000 raw pixel-level annotated images for evaluating object detection using object boundaries. The TUM RGB-D SLAM dataset was proposed by [12]. This database used the MS Xbox Kinect sensor to collect RGB-D frame sequences in two environments with sizes of 6 \(\times\) 6 m\({}^{2}\) and 10 \(\times\) 12 m\({}^{2}\), respectively. The first environment is in the office, and the second environment is in a large industrial hall. This database includes 39 RGB-D frame sequences with a size of 640 \(\times\) 480 pixels and an acquisition rate of 30 Hz, and they are divided into four groups as follows: calibration, testing and debugging, handheld SLAM, and robot SLAM. The ICL-NUIM dataset was proposed by [13]. This database includes RGB-D sequences collected in the living room and the office room by MS Kinect. It has been used to evaluate VO models, 3D reconstruction, and SLAM algorithms. The GT includes 3D camera trajectories and synthetic trajectories data built on RGB-D images and adds noise from color images and noise from depth images. c. Evaluation measures  
To evaluate depth estimation models, the Root Mean Squared Error (\(RMSE\)) measure is often used. The \(RMSE\) is the square root of the average of the squared errors. The \(RMSE\) is the standard deviation of the residuals (prediction error). The residual is a measure of distance from the regression line data points; the \(RMSE\) is a measure of how spread out these residuals are.