In this paper, we present a visual simultaneous localization and mapping (SLAM) system for humanoid robots. We introduce a new binary descriptor called DLab that exploits the combined information of color, depth, and intensity to achieve robustness with respect to uniqueness, reproducibility, and stability. We use DLab within ORB-SLAM, where we replaced the place recognition module with a modification of FAB-MAP that works with newly built codebooks using our binary descriptor. In experiments carried out in simulation and with a real Nao humanoid equipped with an RGB-D camera, we show that DLab has a superior performance in comparison to other descriptors. The application to feature tracking and place recognition reveal that the new descriptor is able to reliably track features even in sequences with seriously blurred images and that it has a higher percentage of correctly identified similar images. As a result, our new visual SLAM system has a lower absolute trajectory error in comparison to ORB-SLAM and is able to accurately track the robot's trajectory.