## 2 Related Works
The general model is optimized for model parameters such as camera poses, camera intrinsics, and geometry parameters. [PERSON] et al. [16] proposed ORB-SLAM, [PERSON] et al. [17] proposed ORB-SLAM2, and [PERSON] et al. [35] proposed ORB-SLAM3. Details of these three techniques are presented below. [PERSON] et al. [36] proposed Maplab as an open framework to build a visual-inertial SLAM. It includes two main components: an online part and an offline part. VIO and localization front end, ROVIOLI's online part, is used to obtain data from the sensor. Maplab-console's offline section is used in a batch offline method with various algorithms. In research on learning-based methods, [PERSON] et al. [37] proposed an online initialization method and a calibration method to optimize a visual-inertial odometry from data obtained from a monocular camera. This method performs state initialization and correction using the space-time constraints of the bootstrapping system. The aligned poses are used to initialize a set of spatial and temporal parameters. [PERSON] et al. [38] proposed the DPLVO method as an improvement of DSO to directly estimate VO using points and lines. The general optimization problem is performed on 3D lines, points, and poses using a sliding window. At the same time, long-term setup is performed on detected keyframes. [PERSON] et al. [39] proposed a method to estimate VO using end-to-end deep learning based on depth and optical flow. The proposed method learns the ground-truth pose states and estimates camera pose with 6-DOF on a space of one-frame-by-one-frame.Lin et al. [40] proposed an unsupervised DL method to estimate 6-DOF VO from a monocular camera. The depth is estimated from DispNet, and a residual-based method is used to perform pose refinement. In particular, the processes of estimating rotation, translation, and scale of the pose are performed separately. [PERSON] et al. [41] proposed WPO-Net to estimate 6-DOF VO from a monocular camera. WPO-Net is a supervised learning-based method and is based on a feature encoder and pose regressor with a convolutional neural network from multiple consecutive two-grayscale-image stacks. [PERSON] et al. [42] proposed SimVODIS to perform three main tasks: VO estimation and object detection and instance segmentation. SimVODIS is a deep network that operates on a stream and exploits shared feature maps with two branches: semantic mapping and data-driven VO. [PERSON] et al. [43] proposed SelfVIO as an adversarial training and self-adaptive visual-inertial sensor fusion method to estimate VO from monocular RGB image sequences. The 6-DOF camera pose is estimated using GANs based on the objective loss function of warping view sequences. We surveyed some of the datasets for evaluating VO methods, shown below. KITTI dataset: The KITTI dataset [9; 10; 11] is the most popular dataset for evaluating visual SLAM and VO models and algorithms. This dataset includes two versions: the KITTI 2012 dataset [10] and the KITTI 2015 dataset [11]. The data from [PERSON] et al. [9] are used to evaluate the VO, object-detection, and object-tracking models. The KITTI 2012 dataset was 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). The KITTI 2012 dataset is also divided into datasets serving different problems. The dataset used to evaluate optical flow estimation models includes 194 image pairs for training and 195 image pairs for testing; the images have a resolution of 1240 \(\times\) 376 pixels, and the ground-truth data are built based on 50% density. The dataset used to evaluate 3D visual odometry/SLAM models consists of 22 stereo sequences collected from a length of 39.2 km of driving. This dataset provides benchmarks and evaluation measures for VO and Visual SLAM such as motion trajectory and driving speed.