Since most people now have a high-performing computing device with an attached camera in their pocket, in the form of a smartphone, robotics and computer vision researchers are thrilled about the possibility this creates. Such devices have previously been used in robotics to create 3D maps of environments and objects by feeding the camera data to a 3D reconstruction algorithm. The big downside with smartphones is that their cameras use a different sensor than what is usually used in robotics, namely a rolling shutter camera.These cameras are cheaper to produce but are not as well suited for general 3D reconstruction algorithms as the global shutter cameras typically used in robotics research. One recent, accurate and performance effective 3D reconstruction method which could be used on a mobile device, if tweaked, is LSD-SLAM. This thesis uses the LSD-SLAM method developed for global shutter cameras and incorporates additional methods developed allow the usage of rolling shutter data.The developed method is evaluated by calculating numbers of failed 3D reconstructions before a successful one is obtained when using rolling shutter data.The result is a method which improves this metric with about 70\% compared to the unedited LSD-SLAM method.