Prior research has demonstrated the effectiveness of end-to-end reinforcement learning for PointGoal navigation tasks within indoor environments. Given 2.5 billion frames of experience, a navigation policy can be trained to achieve a success rate of 0.94 when deployed in unseen environments. However, a limitation of this approach is its reliance on perfect localisation, which is unrealistic for real-world deployment scenarios where localisation must be estimated, inevitably introducing errors. In this paper, we present a study on the effectiveness of integrating a traditional vision-based SLAM algorithm with a reinforcement learning-based PointGoal navigation policy. Through our experimentation, we demonstrate how fine-tuning a pre-trained navigation policy on realistic localisation estimates can increase the success rate by 14% (0.71 \(\rightarrow \) 0.85) and SPL by 15% (0.66 \(\rightarrow \) 0.81) when compared to deploying policies in a zero-shot manner.