# Lifelong Topological Visual Navigation

arXiv: https://arxiv.org/abs/2110.08488 · HTML: https://mlsanity.com/paper/2110.08488/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [Search on the Replay Buffer: Bridging Planning and Reinforcement Learning](https://mlsanity.com/paper/1906.05253/) (2019)
- [Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/1911.08453/) (2019)
- [ViNG: Learning Open-World Navigation with Visual Goals](https://mlsanity.com/paper/2012.09812/) (2020)
- [Bayesian Relational Memory for Semantic Visual Navigation](https://mlsanity.com/paper/1909.04306/) (2019)
- [PyRobot: An Open-source Robotics Framework for Research and Benchmarking](https://mlsanity.com/paper/1906.08236/) (2019)
- [Scaling Local Control to Large-Scale Topological Navigation](https://mlsanity.com/paper/1909.12329/) (2019)
- [Sparse Graphical Memory for Robust Planning](https://mlsanity.com/paper/2003.06417/) (2020)
- [Hallucinative Topological Memory for Zero-Shot Visual Planning](https://mlsanity.com/paper/2002.12336/) (2020)

## Built on by (most cited first, 7 of 7)

- [One-4-All: Neural Potential Fields for Embodied Navigation](https://mlsanity.com/paper/2303.04011/) (2023)
- [TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments](https://mlsanity.com/paper/2402.04061/) (2024)
- [Personalized Embodied Navigation for Portable Object Finding](https://mlsanity.com/paper/2403.09905/) (2024)
- [PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching](https://mlsanity.com/paper/2404.01674/) (2024)
- [RANa: Retrieval-Augmented Navigation](https://mlsanity.com/paper/2504.03524/) (2025)
- [NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot](https://mlsanity.com/paper/2410.11492/) (2024)
- [PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps](https://mlsanity.com/paper/2506.15849/) (2025)

## Abstract (arXiv)

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.