Abstract: For the current monocular visual-inertial SLAM (simultaneous localization and mapping) systems, the acceleration excitation is necessary, and the system accuracy will decrease in the case of high IMU (inertial measurement unit) noises. To solve those problems, a visual-inertial SLAM method based on rolling shutter RGB-D cameras is proposed, named VINS-RSD method, which combines rolling shutter RGB-D image and IMU to initialize the system. The rolling shutter effect is corrected by controlling the feature velocity, and a loss kernel function with confidence factor is applied to the sliding window optimization. An open-source depth dataset extended from WHU-RSVI dataset is developed to evaluate the RGB-D visual-inertial SLAM method. Experiments are performed on the dataset and the root mean square error of VINS-RSD method is reduced by 30.76% compared to VINS-Mono (monocular visual-inertial system) method, which demonstrates that the proposed method can achieve a higher tracking accuracy.