From Pixels to Maps: Implementing RGB-D SLAM with Kinect on a Mobile Robot
The advancement of robotics has expanded applications across various sectors, increasing the need for reliable mapping and navigation in unfamiliar environments. Simultaneous Localization and Mapping (SLAM) enables mobile robots to estimate their position while constructing an environmental map, while RGB-D SLAM combines visual and depth information for three-dimensional perception. This study implements and evaluates an RGB-D SLAM system using a Microsoft Kinect for Xbox 360 integrated with the Robot Operating System (ROS) on a differential-drive mobile robot. The system was evaluated through Gazebo simulations and real-world experiments under four conditions: tidy indoor, cluttered indoor, dynamic indoor, and outdoor environments. The system successfully generated 2-D octomaps and 3-D meshes across the tested conditions. Quantitative evaluation showed that the selected RTAB-Map configuration, with a queue size of 20 and an odometry maximum rate of 10, resulted in mean memory-update and data-compression times of 56.9 s and 10.6 s, respectively. The Kinect exhibited an effective mapping range of approximately 1–3 m, with a blind region below 1 m. The results demonstrate the feasibility of combining a low-cost RGB-D sensor with ROS-based SLAM for practical 2-D and 3-D mapping while also highlighting limitations associated with occlusion, dynamic objects, and sensor range.