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It is fundamental for personal robots to reliably navigate to a specifiedgoal. To study this task, PointGoal navigation has been introduced in simulatedEmbodied AI environments. Recent advances solve this PointGoal navigation taskwith near-perfect accuracy (99.6% success) in photo-realistically simulatedenvironments, assuming noiseless egocentric vision, noiseless actuation, andmost importantly, perfect localization. However, under realistic noise modelsfor visual sensors and actuation, and without access to a "GPS and Compasssensor," the 99.6%-success agents for PointGoal navigation only succeed with0.3%. In this work, we demonstrate the surprising effectiveness of visualodometry for the task of PointGoal navigation in this realistic setting, i.e.,with realistic noise models for perception and actuation and without access toGPS and Compass sensors. We show that integrating visual odometry techniquesinto navigation policies improves the state-of-the-art on the popular HabitatPointNav benchmark by a large margin, improving success from 64.5% to 71.7%while executing 6.4 times faster.

The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation