In this work, a ROS (Robot Operating System) version of OpenRatSLAM, [1] [2], was tested with Viso2 [3] as an alternative monocular odometer. A land based rover [4] was used to perform data acquisition and a remote control tool was developed to facilitate this procedure, implemented as ROS nodes on both Ubuntu 16.04 and on Android 7.0.  An additional requirement that comes from using Viso2 is the need for camera information together with the image stream, which might require camera calibration. A ROS node to manually add this camera information was made as well as a node to change the generated odometry message from Viso2 to a form that RatSLAM uses. The implemented odometer uses feature tracking to estimate motion, which is fundamentally different to matching intensity profiles which the original method does and can hence be used when different properties of the visual odometry function is desired. From experiments, it was seen that the feature tracking method from Viso2 generated a more robust motion estimate in terms of real world scale and it was also able to better handle environments of varying illumination or that contains large continuous surfaces of the same colour. However, the feature tracking may give slight variations in the generated data upon successive runs due to the random selection of features to track. Since the structure of RatSLAM gives the system ability to make loop closures even with large differences in position, an alternative odometry does not necessarily give a significant improvement in performance of the system in environments that the original system operates well in. Even though both algorithms show difficulty with estimating fast rotations, especially when the camera view contains areas with few features, the performance improvement in Viso2 together with its ability to better maintain the real-world scale motivates its usefulness.  The source code, as well as instructions for installation and usage is public.