# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 1 Introduction  
In recent years, simultaneous localization and mapping (SLAM) has developed into a research hotspot in the field of mobile robots. It is considered to be the core link to achieve autonomous navigation. SLAM includes two main tasks, namely positioning and mapping robots in an unknown environment; the pose of the robot is obtained by detecting the surrounding features of the sensor during the movement process, and then the map of the environment is constructed from the robot's point of view [1, 2]. Visual SLAM can be divided into direct methods and feature methods to estimate camera motion based on the obtained images. Direct methods, such as DTAM [3], LSD-SLAM [4] and DSO [5], estimate camera motion according to the pixel brightness information of the image, and optimize the solution by minimizing the brightness error. However, its prerequisite is based on the assumption of the invariant gray level, that is, the pixel gray level of the same spatial point is fixed in successive image frames. The point feature method mainly uses SIFT [6], ORB [7] or SURF [8] to extract and match point features. According to the result of feature matching, incremental beam adjustment is used to minimize the reprojection error to estimate the camera pose, such as PTAM [9] and ORB-SLAM2 [10]. However, the shortcomings of pure visual SLAM are also obvious. It is more sensitive to situations where the movement speed is too fast, the rotation is too intense, and the exposure is too high. The IMU inertial measurement unit can obtain more accurate motion information because the acquisition frequency is higher than that of the camera, but the IMU also has drift. The effective combination of the two can make up for the shortcomings of the visual degradation of the camera and the drift of the correction IMU, so as to provide better data information. To solve these problems, researchers combine vision and IMU and propose a number of tightly coupled visual-inertial SLAM methods that jointly optimizevision and IMU information, such as MSCKF [11], VINS-Mono [12] and ORB-SLAM3 [13] and so on. VINS-Mono, proposed by [PERSON] in 2018, is a sliding window estimator based on nonlinear optimization. It closely integrates pre-integrated IMU measurements with visual observation, minimizes the sum of IMU measurements and visual measurement residuals, and obtains the maximum posteriori estimation. By solving nonlinear problems, the attitude and deviation of the IMU can be calculated. [PERSON] et al. proposed ORB-SLAM3 in 2020. This system integrates IMU on the basis of ORB-SLAM2, which greatly improves its performance. It is one of the most advanced visual-inertial SLAM systems based on the feature point method. In addition, there are often rich line segment features in indoor and other artificial environments, so many researchers add line features to the data association between images. [PERSON] [14] et al. implemented visual odometry for point-line feature fusion based on RGB-D cameras, and proved that point-line features have less motion uncertainty than single features. For the first time, [PERSON] [15] adopted the orthogonal representation of the minimum parameterized line in SLAM, and analytically derived the Jacobian matrix of the reprojection error relative to the line parameters, which greatly improved the SLAM solution. [PERSON] et al. proposed the PL-SLAM [16] method based on a binocular camera, which uses point-line features in all processes, including visual odometer, key frame selection, and beam adjustment, and proposed a new word bag model, which comprehensively considers the information of point-line features in the closed-loop process. [PERSON] et al. proposed the PL-VIO [17] method based on the VINS-Mono method, which optimized the point-line features and IMU information in a tightly coupled manner, superior to VIO systems based only on point-features. [PERSON] et al.