# Is Mapping Necessary for Realistic PointGoal Navigation?

arXiv: https://arxiv.org/abs/2206.00997 · HTML: https://mlsanity.com/paper/2206.00997/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2004.02857/) (2020)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias](https://mlsanity.com/paper/1807.07049/) (2018)
- [Auxiliary Tasks Speed Up Learning PointGoal Navigation](https://mlsanity.com/paper/2007.04561/) (2020)
- [PyRobot: An Open-source Robotics Framework for Research and Benchmarking](https://mlsanity.com/paper/1906.08236/) (2019)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents](https://mlsanity.com/paper/2009.03231/) (2020)
- [The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation](https://mlsanity.com/paper/2108.11550/) (2021)
- [Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?](https://mlsanity.com/paper/1912.06321/) (2019)
- [Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation](https://mlsanity.com/paper/2103.08022/) (2021)
- [Large Batch Simulation for Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07013/) (2021)
- [Estimating Uncertain Spatial Relationships in Robotics](https://mlsanity.com/paper/1304.3111/) (2013)
- [How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget](https://mlsanity.com/paper/2012.06117/) (2020)

## Built on by (most cited first, 30 of 35)

- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [Retrospectives on the Embodied AI Workshop](https://mlsanity.com/paper/2210.06849/) (2022)
- [OctoNav: Towards Generalist Embodied Navigation](https://mlsanity.com/paper/2506.09839/) (2025)
- [Emergence of Maps in the Memories of Blind Navigation Agents](https://mlsanity.com/paper/2301.13261/) (2023)
- [Phone2Proc: Bringing Robust Robots Into Our Chaotic World](https://mlsanity.com/paper/2212.04819/) (2022)
- [ViNL: Visual Navigation and Locomotion Over Obstacles](https://mlsanity.com/paper/2210.14791/) (2022)
- [PEANUT: Predicting and Navigating to Unseen Targets](https://mlsanity.com/paper/2212.02497/) (2022)
- [Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation](https://mlsanity.com/paper/2308.10306/) (2023)
- [Object Goal Navigation with Recursive Implicit Maps](https://mlsanity.com/paper/2308.05602/) (2023)
- [ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments](https://mlsanity.com/paper/2407.18550/) (2024)
- [Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation](https://mlsanity.com/paper/2402.17587/) (2024)
- [MOPA: Modular Object Navigation with PointGoal Agents](https://mlsanity.com/paper/2304.03696/) (2023)
- [Vision and Language Navigation in the Real World via Online Visual Language Mapping](https://mlsanity.com/paper/2310.10822/) (2023)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [Exploiting Proximity-Aware Tasks for Embodied Social Navigation](https://mlsanity.com/paper/2212.00767/) (2022)
- [Modality-invariant Visual Odometry for Embodied Vision](https://mlsanity.com/paper/2305.00348/) (2023)
- [Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction](https://mlsanity.com/paper/2306.03857/) (2023)
- [Learning-Augmented Model-Based Planning for Visual Exploration](https://mlsanity.com/paper/2211.07898/) (2022)
- [Learning to navigate efficiently and precisely in real environments](https://mlsanity.com/paper/2401.14349/) (2024)
- [IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation](https://mlsanity.com/paper/2511.17384/) (2025)
- [Following the Human Thread in Social Navigation](https://mlsanity.com/paper/2404.11327/) (2024)
- [NOLO: Navigate Only Look Once](https://mlsanity.com/paper/2408.01384/) (2024)
- [BEVNav: Robot Autonomous Navigation Via Spatial-Temporal Contrastive Learning in Bird's-Eye View](https://mlsanity.com/paper/2409.01646/) (2024)
- [Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation](https://mlsanity.com/paper/2212.08801/) (2022)
- [LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation](https://mlsanity.com/paper/2602.02220/) (2026)
- [SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment](https://mlsanity.com/paper/2310.12031/) (2023)
- [Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection](https://mlsanity.com/paper/2509.17877/) (2025)
- [Simultaneous Localization and Affordance Prediction of Tasks from Egocentric Video](https://mlsanity.com/paper/2407.13856/) (2024)
- [MPVO: Motion-Prior based Visual Odometry for PointGoal Navigation](https://mlsanity.com/paper/2411.04796/) (2024)

## Abstract (arXiv)

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). 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. First, we identify the main (perhaps, only) cause of the drop in performance: the 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 the 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.