A robotic vehicle must continuously determine its position within the map to traverse a path safely; this is called self-localization. Current localization methods use mainly sensors like LIDARS. However, a LIDAR does not return data points if the environment is an empty field; the laser scan of the LIDAR does not reflect without obstacles, making self-localization in those environments impossible. Self-localization with visual information from cameras can be an alternative for vehicles operating in an open environment, such as the Lely Juno, an agricultural robot operating on farms. A widely used visual Simultaneous Localization and Mapping (SLAM) method in literature, ORB-SLAM3, lacks robustness on Lely Juno's visual data; the track is lost multiple times due to a sudden drop in the number of feature matches. This work aims to experimentally determine (a) the cause of the drop in the feature matches that eventually causes the tracking error and (b) the robustness and accuracy of ORB-SLAM3 compared to a simple visual odometry (VO) system. The cause of the drop in feature matches is investigated outside the ORB-SLAM3 pipeline by replicating the matching thread of ORB-SLAM3 with OpenCV ORB feature matching. The robustness and accuracy of ORB-SLAM3 are compared with a simple visual odometry system by comparing the trajectories and the minimum number of matches found in the sequences. The sudden drops in feature matches could not be replicated outside the ORB-SLAM3 pipeline with the brute-force-based ORB matcher in OpenCV. While in some cases, the decrease in feature matches is related to the image itself (e.g., blur, movement), the leading cause is complicated. In this work, the leading cause is further reduced to the quality of the disparity map, the type of matcher used, and the use of grids when extracting features. The simple VO system is more robust than ORB-SLAM3. However, the absolute pose error is worse and unsuitable for reliable navigation for long trajectories. For the Lely Juno, a simple visual odometry system can only be used for short distances outside. Therefore, investigating the issues in ORB-SLAM3 is an excellent direction from the project's point of view. More understanding is needed of the exact effects of the disparity map, the type of matcher used, and the use of grids on the number of feature matches. The contributions of this work are insights into the reasons behind the tracking errors, exploring the effect of different ORB parameters and datasets on the matching performance, and creating two new datasets in low-textured and repetitive agricultural environments. Investigating the exact impact of the disparity map, the type of matcher used, and the use of grids on the number of feature matches is left for future work.