The ability to navigate in complex environments is a fundamental skill of a home robot. Despite extensive study, indoor navigation in unseen environments under noisy actuation and sensing and without access to precise localization continues to be an open frontier for research in Embodied AI. In this work, we focus on designing a visual odometry module for robust egomotion estimation and it’s integration with navigation policy for efficient navigation under noisy actuation and sensing. Specifically, we study how the observations transformations and incorporating meta-information available to the navigation agent impacts visual odometry model generalization performance. We present a set of regularization techniques that can be implemented as train- and test-time augmentations to increase the robustness to noise. Navigation agent, equipped with our visual odometry module, reaches the goal in 86% of episodes and scores 0.66 SPL in Habitat Challenge 2021 benchmark.