## 1 Introduction
It furnishes the essential three-dimensional geometric data necessary for constructing environment maps and plays a pivotal role in realizing the perception, positioning, and navigation functions of the SLAM algorithm within the environment. Typically, these 3D reconstruction representations take the form of point cloud models, mesh models, and geometric models. For example, Kinect Fusion [12] can use the RGB-D Kinect camera to achieve instant 3D reconstruction of the environment through dense point cloud sampling and real-time tracking algorithms. Furthermore, algorithms from the LOAM series can also find applications in the Architecture, Engineering, and Construction (AEC) field [13]. The dataset determines the upper limit of the algorithm, so another focus of this paper is to record datasets using 3D LiDAR scans and RGB-D camera streams. Current public datasets differ with target scenes, recorded sensor data, and ground truth collection methods. For example, the TUM (Technical University of Munich) dataset [14; 15] is suitable for pure Visual SLAM and visual-IMU SLAM, where the motion capture system records the ground truth. Another example is the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset [16], which also provides high-quality data. KITTI employs Real-Time Kinematic GPS (RTK-GPS, [17]) in conjunction with Inertial Navigation Systems (INS) to establish ground truth data. But it uses a stereo camera sensor instead of an RGB-D camera sensor. In previous SLAM research or common service robots in the market, SLAM technology is often used only in one specific scenario. As SLAM technology advances with modern technology, robots are required to continuously navigate between indoor and outdoor environments. However, different environments may be suitable for different SLAM algorithms. Our research aims to assist researchers in gaining a more comprehensive understanding of the performance of various SLAM approaches. Moreover, the literature [2; 18], is also useful for us to choose suitable locations for conducting multiple experiments, thereby enhancing the variability and assessing the performance of various SLAM systems. The summary of this research work can be succinctly encapsulated as follows:  
(a) A systematic review of the SLAM algorithms including Gmapping, ORB-SLAM3, SC-LeGO-LOAM, and Cartographer is introduced for the comparison. (b) The robot platform used for implementation, the process to establish datasets including RGB-D and 3D LiDAR data, and the mapping approach utilized in this study are introduced. In addition, the benchmark calculated by the Cartographer algorithm for the trajectory error estimation is acquired. (c) The comparative analysis is divided into three main sections: localization, mapping, and performance evaluation. The localization focuses on evaluating the accuracy of pose tracking by ATE and RPE, which can be acquired by calculating the Euclidean distance between the ground-truth poses and the estimated poses [19]. The mapping section focuses on evaluating 3D reconstruction performance, considering factors such as the quality of reconstructed models and adaptability to varying environmental conditions. Additionally, the performance evaluation encompasses metrics such as CPU utilization and memory usage. Effective resource management is deemed crucial for real-time applications and overall system usability.