A Combined RGB and Depth Descriptor for SLAM with Humanoids

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.