This master thesis, conducted at SAAB Dynamics Linköping, studies Visual Odometry (VO), or camera pose estimation, using a monocular event camera. Event cameras are not yet widely used in the industry, and there is significant interest in understanding the methodological differences between performing Visual Odometry (VO) with event cameras compared to traditional frame cameras. Event cameras have the potential to capture information between frames, which may include data that is lost or not captured by frame cameras. This thesis compares two different feature detectors and evaluates their performance against a frame-based method. Visual Odometry was conducted both with and without known 3D points. However, attempts to perform VO without known 3D points did not yield a robust pipeline within the limited time frame of this thesis, and thus was not further developed to function with a purely event-based method. On the other hand, VO performance with known 3D points achieved continues 6-DoF pose estimation. The results demonstrate that event cameras have the potential to detect and track features in challenging scenes, such as those with dark or bright lighting conditions, for example, objects passing by the sun. This thesis suggests that implementing a robust 6-DoF pose estimation would be feasible with a more reliable 3D-2D point pair matching technique or a more sophisticated VO pipeline.