# The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation

arXiv: https://arxiv.org/abs/2108.11550 · HTML: https://mlsanity.com/paper/2108.11550/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 58 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 30 of 45)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [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)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [Modelling Uncertainty in Deep Learning for Camera Relocalization](https://mlsanity.com/paper/1509.05909/) (2015)
- [DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch](https://mlsanity.com/paper/1909.05845/) (2019)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [Embodied Question Answering in Photorealistic Environments with Point Cloud Perception](https://mlsanity.com/paper/1904.03461/) (2019)
- [Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments](https://mlsanity.com/paper/1910.14442/) (2019)

## Built on by (most cited first, 27 of 27)

- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)
- [GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation](https://mlsanity.com/paper/2404.06609/) (2024)
- [Cross-modal Map Learning for Vision and Language Navigation](https://mlsanity.com/paper/2203.05137/) (2022)
- [Housekeep: Tidying Virtual Households using Commonsense Reasoning](https://mlsanity.com/paper/2205.10712/) (2022)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification](https://mlsanity.com/paper/2212.00338/) (2022)
- [Is Mapping Necessary for Realistic PointGoal Navigation?](https://mlsanity.com/paper/2206.00997/) (2022)
- [OctoNav: Towards Generalist Embodied Navigation](https://mlsanity.com/paper/2506.09839/) (2025)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [Episodic Memory Question Answering](https://mlsanity.com/paper/2205.01652/) (2022)
- [DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation](https://mlsanity.com/paper/2411.04999/) (2024)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [PEANUT: Predicting and Navigating to Unseen Targets](https://mlsanity.com/paper/2212.02497/) (2022)
- [Memory-based Adapters for Online 3D Scene Perception](https://mlsanity.com/paper/2403.06974/) (2024)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [LookOut: Real-World Humanoid Egocentric Navigation](https://mlsanity.com/paper/2508.14466/) (2025)
- [Break and Make: Interactive Structural Understanding Using LEGO Bricks](https://mlsanity.com/paper/2207.13738/) (2022)
- [Object Manipulation via Visual Target Localization](https://mlsanity.com/paper/2203.08141/) (2022)
- [Modality-invariant Visual Odometry for Embodied Vision](https://mlsanity.com/paper/2305.00348/) (2023)
- [Robust Navigation with Cross-Modal Fusion and Knowledge Transfer](https://mlsanity.com/paper/2309.13266/) (2023)
- [LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation](https://mlsanity.com/paper/2602.02220/) (2026)
- [POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation](https://mlsanity.com/paper/2605.28237/) (2026)
- [Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment](https://mlsanity.com/paper/2210.00413/) (2022)
- [The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry](https://mlsanity.com/paper/2406.01797/) (2024)
- [MPVO: Motion-Prior based Visual Odometry for PointGoal Navigation](https://mlsanity.com/paper/2411.04796/) (2024)
- [Insect-inspired Visual Point-goal Navigation](https://mlsanity.com/paper/2601.16806/) (2026)
- [Macro-Action Topological Navigation under Noisy Localization using Reinforcement Learning](https://mlsanity.com/paper/2608.23055/) (2026)

## Abstract (arXiv)

It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environments, assuming noiseless egocentric vision, noiseless actuation, and most importantly, perfect localization. However, under realistic noise models for visual sensors and actuation, and without access to a "GPS and Compass sensor," the 99.6%-success agents for PointGoal navigation only succeed with 0.3%. In this work, we demonstrate the surprising effectiveness of visual odometry for the task of PointGoal navigation in this realistic setting, i.e., with realistic noise models for perception and actuation and without access to GPS and Compass sensors. We show that integrating visual odometry techniques into navigation policies improves the state-of-the-art on the popular Habitat PointNav benchmark by a large margin, improving success from 64.5% to 71.7% while executing 6.4 times faster.