# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 1 Introduction  
Simultaneous localization and map construction technology, as the key to autonomous movement of robots, is widely used in unmanned driving, virtual reality, mobile robots, and other fields [1; 2; 3; 4; 5]. Compared with laser SLAM, vision-based SLAM has a low power consumption, low cost, miniaturization, and other advantages, and its theoretical and application value is very prominent [6]. Visual SLAM constructs a map of the surrounding environment by obtaining the plane image information of the real world through a camera. The pose state of the camera is inferred by the extracted feature information or pixel grayscale. Visual SLAM methods can be classified into four categories: feature-point-based methods [6; 7; 8], feature-line-based methods [9; 10; 11; 12], feature-plane-based methods [13; 12], and the combination of the above methods [14; 15; 16; 17; 18; 19; 20], according to the kind of features used to estimate the trajectory. The existing empirical methods cannot deal effectively with complex industrial scenarios due to occlusion, illumination, and deformation issues. In recent years, research on visual SLAM based on deep learning has attracted widespreadattention. [PERSON] et al. [21] used deep convolution to extract a point description with the L2 norm as a similarity measure to enhance the robust matching between key points and their local image features. Since a single feature descriptor cannot generate an effective key-point detection, [PERSON] et al. [22] proposed an end-to-end trainable matching network, RF-Net, based on a receptive field to achieve a more efficient key-point detection. In order to solve the problem of the low localization accuracy caused by the lack of key-point shape perception in the joint learning of feature detectors and descriptors, [PERSON] et al. [23] applied a deformable convolutional network with a dense spatial transformation to enhance the dynamic receptive field and improve the ability to express local shapes. [PERSON] et al. [24] proposed a graph neural network, SuperGlue, based on attention aggregation, which used the optimal transmission model for matching optimization and realized the pose estimation in both indoor and outdoor environments. Combining a convolutional network with a recurrent network, [PERSON] et al. [25; 26] proposed a geometric correspondence network (GCN), which used an end-to-end learning method to detect key points and generate descriptors for improving the accuracy of the pose estimation. Aimed at the problem of decreased localization accuracy caused by the partial occlusion of line segments, [PERSON] et al. [27] used a self-supervised network for line detection and for extracting line-segment descriptors, which improved the robustness of line-segment matching. The above-mentioned feature detection algorithms based on deep learning fused multilevel features, which did not deeply explore the association and constraint relationship between point and line features. In industrial production scenarios, there are many complex background objects such as various buildings, pipelines, production equipment, and safety signs that lack corner points or contain repeated textures. The point features in the image are not specific enough, which makes them unable to provide an accurate position estimation. In addition, the mismatch of line features greatly increases the time complexity of the computation. In general, although existing methods have achieved certain results in feature detection and matching tasks, due to the uneven light, single texture, and complex scene structure in industrial scenarios, the pose estimation is easily degraded. How to efficiently fuse point and line information to build a more stable visual SLAM system is still a difficult problem that needs further research. Our main contributions can be summarized as follows:  
* To solve the problem of weak point-line extraction ability in complex scenes, a point-line synchronous geometric feature extraction network, PL-Net, is proposed.