# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
###### Abstract
ORB-SLAM2 [10], proposed in 2017, supports SLAM systems for monocular, stereo, and RGB-D cameras, and it can work in real time on CPUs. ORB-SLAM3 [11], proposed in 2021, supports the use of various data sources such as vision, vision plus inertial, and hybrid maps. It can perform calculations on monocular, binocular, and RGB-D cameras on pinhole or fisheye models for real-time computation. In addition, ORB-SLAM3 supports deep learning and GPU acceleration to speed up the system's processing speed and operational efficiency. In addition to sparse and semi-dense maps, some systems can build dense maps, e.g., ElasticFusion [12], which is a technique for 3D dense reconstruction that uses a surface element model called surfel. For RGB images, ElasticFusion computes the position by using color consistency constraints, while for point cloud data, an iterative nearest-point approach is used to compute the position. This approach enables efficient position estimation and 3D reconstruction while also maintaining accuracy and robustness in complex environments. The Kinect Fusion [13] is the first Kinect-based SLAM algorithm that builds dense 3D maps on the GPU in real time. The algorithm utilizes only the depth information acquired by the Kinect camera to compute the positional pose and build an accurate map model of the environment. Real-Time Appearance-Based Mapping (RTAB-Map) [14] is one of the classic programs for RGB-D SLAM. It can build point cloud maps and triangular grid maps, and its program is available from the ROS system. Although the application of visual SLAM technology for localization and navigation has had many research achievements in recent years, there are still many problems and challenges. It has real-time performance in simple environments such as static, rigid, insignificant lighting changes, or no interference from high-speed moving objects and can generate maps in a short amount of time. However, in complex scenes, robots are often disturbed by various noises when observing the external environment, such as indoor dynamic environments, changes in the camera observation angle, and changes in lighting. These disturbances may lead to errors in the visual localization of the robots or incorrectly constructed environmental structures, which has a detrimental effect on visual navigation. With the significant progress made in image processing by deep learning in artificial intelligence, there has been related research to apply deep learning techniques into visual SLAM to utilize high-level features learned in deep neural networks for image inter-frame estimation, loop closing, and mapping. Inter-frame estimation is a front-end process in visual SLAM, also known as visual odometry. In contrast to traditional inter-frame estimation methods, which generally require the extraction of sparse or dense features, deep learning-based methods are more intuitive and concise, as they no longer require the extraction of feature points, matching of feature points, and computation of geometric relationships. Currently, DeepVO [15] is one of the most effective and widely used visual odometry methods based on deep learning. DeepVO can infer the camera's position directly from image sequences, it uses a convolutional neural network to learn the image features, and it uses a deep recurrent neural network to learn the dynamic relationships between images. Loop closing is a key step in visual SLAM, which determines whether a trajectory forms a loop by using sensor information to detect whether the robot has returned to a previously passed location. Loop closing is essentially a recognition problem, and traditional recognition methods use pre-designed features for matching recognition, while recognition methods using deep learning can improve recognition accuracy and efficiency by learning deep features in an image and comparing the image at the pixel level.