## 1 Introduction  
SLAM has emerged as a prominent subject within the robotics community in recent years, leading to the development of numerous associated open-source projects. These projects encompass a wide range of applications, including autonomous vehicles, drones, architectural surveying, and scene scanning. SLAM stands for Simultaneous Localization and Mapping, representing a vital technique with immense potential. It enables a robot to robustly ascertain its position (localization) while concurrently constructing a real-time representation of previously unexplored environments (mapping) [1]. Various sensors are employed to gauge distances in the real world and capture pertinent features. Among these sensors, cameras and LiDAR systems are the most prevalent. Consequently, the SLAM domain primarily encompasses visual-based SLAM and LiDAR-based SLAM, contingent upon the input from the specific sensors. In our previous study, we discussed mainly mapping for mobile robots in outdoor environments involving LiDAR SLAM and Visual SLAM [2]. Thus, this study primarily revolves around the performance comparison between Visual SLAM and LiDAR SLAM in terms of localization and mapping for mobile robots operating in real-world environments. With an emphasis on maintaining continuity and universality, we strive to opt for cost-effective solutions. Therefore, the chosen algorithm representatives for these two SLAM methods are ORB-SLAM3 [3] and SC-LeGO-LOAM [4], respectively. Visual SLAM (VSLAM) mainly infers the motion of the camera and the surrounding environment based on the continuous color image stream. To ensure minimal distortion and reduce calculations related to the epipolar geometry-based positional relationships, depth image streams are often integrated into the camera's positioning. As exemplified by our research focus, ORB-SLAM3 [3] is a feature-based SLAM system that employs pinhole and fisheye lens models to facilitate visual, visual-inertial, and multi-map SLAM by monocular, stereo, and RGB-D cameras. Based mainly on ORB-SLAM2 [5] and ORB-SLAM visual-inertial [6], ORB-SLAM3 introduces several novel methods, including a multi-map system, bag-of-words for loop closure, and advanced mid-term and long-term data association techniques. LiDAR SLAM can be roughly divided into 2D and 3D LiDAR SLAM, generating 2D and 3D maps separately. Early LiDAR SLAM systems, such as Gmapping [7], and Simultaneous Localization, Mapping, and Moving Object Tracking (SLAM-MOT) [8] primarily utilized 2D LiDAR sensors and were often based on the Extended Kalman Filter (EKF) algorithm [9]. EKF is the nonlinear version of the Kalman Filter (KF), which approximates the state of a nonlinear system through a linear system that employs a first-order Taylor expansion. As hardware advances and algorithms become increasingly refined, optimization-based techniques and the integration of multiple scanning channels with 3D LiDAR play increasingly significant roles in the field of LiDAR SLAM. SC-LeGO-LOAM [4] is an expansion of the LOAM (Lidar Odometry And Mapping) [10] approach and incorporates a scan context loop detection technique [11] that utilizes a global descriptor obtained through the scan context to detect loops. The main objective of SC-LeGO-LOAM is to perform real-time odometry and mapping by efficiently processing the point cloud data acquired from a 3D LiDAR sensor and to achieve accurate and robust localization and mapping. An Inertial Measurement Unit (IMU) can also be used as a supplement to the SLAM position relationship, providing vision with fast-moving positioning. However, the external parameter matrix from the camera or LiDAR to IMU must be obtained through physical measurement and sensor calibration. If the external reference calibration is not accurate enough, the positioning effect of the experimental restoration may be seriously wrong. Therefore, the experiments involved in this paper did not use IMU data. Three-dimensional reconstruction serves as both an application and a component of SLAM technology.