# From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)

arXiv: https://arxiv.org/abs/2002.11310 · HTML: https://mlsanity.com/paper/2002.11310/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations](https://mlsanity.com/paper/2211.16309/) (2022)
- [Task-Driven Graph Attention for Hierarchical Relational Object Navigation](https://mlsanity.com/paper/2306.13760/) (2023)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2304.02738/) (2023)

## Abstract (arXiv)

Visual Indoor Navigation (VIN) task has drawn increasing attention from the data-driven machine learning communities especially with the recently reported success from learning-based methods. Due to the innate complexity of this task, researchers have tried approaching the problem from a variety of different angles, the full scope of which has not yet been captured within an overarching report. This survey first summarizes the representative work of learning-based approaches for the VIN task and then identifies and discusses lingering issues impeding the VIN performance, as well as motivates future research in these key areas worth exploring for the community.