## 4 Experiments  
### Hardware  
The robot is designed with a heavy-duty motor, specifically intended for high-load applications, ensuring enhanced torque and operational reliability. This motor is part of the overall design that prioritizes the robot's capability to handle demanding tasks such as transporting heavy loads or navigating rugged terrains. Accompanying the motor system is an advanced computational setup, which includes the Jetson Nano as the core processing unit and high-quality sensors such as the Orbbec Dabai camera for vision-based tasks. Additionally, the robot is equipped with a microphone array module and an N100 9-axis gyroscope module, which provide essential sensing and stabilization functions during operation. The physical object is shown in Figure 9.  
In this study, the selected experimental scene measures approximately 10 m in length and 8 m in width, featuring distinct open areas, obstacle regions, and clear boundaries, allowing for an intuitive comparison of the mapping results. The robot employed three different mapping methods for environmental scanning: gmapping for LiDAR-based mapping, the RTAB-Map using only visual point cloud data for purely visual mapping, and the RTAB-Map for visual and LiDAR fusion. For the pure LiDAR mapping, the gmapping algorithm was employed. This algorithm allows for real-time construction of indoor maps with high accuracy and low computational demand, especially in small environments. Unlike Hector SLAM, which requires higher LiDAR frequency and is prone to errors during rapid robot movements, gmapping uses fewer particles and does not require loop closure detection, reducing computational load while maintaining a high degree of mapping accuracy. Additionally, gmapping effectively utilizes wheel odometry to provide prior pose information, reducing the dependency on high-frequency LiDAR data. For pure visual mapping, the RTAB-Map was employed, using only the visual point cloud data from the RGB-D camera. While this visual system offers detailed environmental features, it is sensitive to lighting conditions, leading to less accurate results in low-light or feature-sparse areas compared to LiDAR-based methods. As the robot navigates through the environment, the map is generated using data from the RGB-D camera. Although the mapping results can be relatively accurate, the system's sensitivity to lighting can cause it to miss key features in areas with insufficient or excessive light, resulting in gaps or inaccuracies in the map. Additionally, in narrow or confined spaces, the camera's limited field of view often prevents the creation of a complete map, as crucial environmental details may not be adequately captured. These limitations highlight the challenges of relying solely on visual mapping, especially in complex or poorly lit environments. In the case of LiDAR and visual fusion mapping, the RTAB-Map was used to integrate LiDAR data with visual information from cameras, resulting in a more comprehensive environmental map. LiDAR provides precise distance measurements and obstacle detection, while the camera offers detailed visual features, enhancing the overall resolution and accuracy of the map, particularly in environments with complex textures. The experimental results show that this fusion of sensors produces a more complete and reliable map. By accurately projecting 3D spatial points onto a 2D plane and processing the depth camera  
Figure 9: Illustration of the system components: (**a**) the robot and (**b**) the charging station. data, the map updates remain consistent with those generated by LiDAR. This fusion method combines the rich visual details captured by the camera with the precise distance measurements and obstacle detection from LiDAR, delivering a more comprehensive representation of the environment. Compared to maps created by single sensors, the fusion approach offers a higher level of environmental awareness, providing a more realistic and robust depiction of the experimental scene.