Can an autonomous agent navigate in a new environment without building an explicit map?

For the task of PointGoal navigation (Go to $\Delta x$, $\Delta y$) under idealized settings (no RGB-D and actuation noise, perfect GPS+Compass), the answer is a clear `yes` — map-less neural models composed of task-agnostic components (CNNs and RNNs) trained with large-scale reinforcement learning achieve 100% Success on a standard dataset (Gibson [1]). However, for PointNav in a realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open question; one we tackle in this paper. The strongest published result for this task is 71.7% Success [2]¹.

First, we identify the main (perhaps, only) cause of the drop in performance: absence of GPS+Compass. An agent with perfect GPS+Compass faced with RGB-D sensing and actuation noise achieves 99.8% Success (Gibson-v2 val). This suggests that (to paraphrase a meme) robust visual odometry is all we need for realistic PointNav; if we can achieve that, we can ignore the sensing and actuation noise.

With that as our operating hypothesis, we scale dataset and model size, and develop human-annotation-free data-augmentation techniques to train models for visual odometry. We advance the state of art on the Habitat Realistic PointNav Challenge from 71% to 94% Success (+23, 31% relative) and 53% to 74% SPL (+21, 40% relative). While our approach does not saturate or `solve` this dataset, this strong improvement combined with promising zero-shot sim2real transfer (to a LoCoBot) provides evidence consistent with the hypothesis that explicit mapping may not be necessary for navigation, even in a realistic setting.

¹According to Habitat Challenge 2020 PointNav benchmark held annually. A concurrent as-yet-unpublished result has reported 91% Success on 2021's benchmark, but we are unable to comment on the details because an associated report is not available.