Visual SLAM using multiple RGB-D cameras

In this paper, we present a solution to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. In the SLAM system, we integrate visual and depth measurements from those RGB-D cameras to achieve more robust pose tracking and more detailed environmental mapping in unknown environments. We present the mathematical analysis of the iterative optimizations for pose tracking and map refinement of a RGB-D SLAM system in multi-camera cases. The resulted SLAM system allows configurations of multiple RGB-D cameras with non-overlapping fields of view (FOVs). Furthermore, we provide a SLAM-based semiautomatic method for extrinsic calibration among such cameras. Finally, the experiments in complex indoor scenarios demonstrate the efficiency of the proposed visual SLAM algorithm.