# A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation

arXiv: https://arxiv.org/abs/1803.04119 · HTML: https://mlsanity.com/paper/1803.04119/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [VTNet: Visual Transformer Network for Object Goal Navigation](https://mlsanity.com/paper/2105.09447/) (2021)
- [Vision-Language Navigation: A Survey and Taxonomy](https://mlsanity.com/paper/2108.11544/) (2021)
- [BuildingNet: Learning to Label 3D Buildings](https://mlsanity.com/paper/2110.04955/) (2021)
- [SGAligner : 3D Scene Alignment with Scene Graphs](https://mlsanity.com/paper/2304.14880/) (2023)
- [SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2404.00469/) (2024)
- [Agent-Centric Relation Graph for Object Visual Navigation](https://mlsanity.com/paper/2111.14422/) (2021)
- [VeriGraph: Scene Graphs for Execution Verifiable Robot Planning](https://mlsanity.com/paper/2411.10446/) (2024)
- [Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion](https://mlsanity.com/paper/2507.08112/) (2025)
- [Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion](https://mlsanity.com/paper/2509.08095/) (2025)

## Abstract (arXiv)

We present a semantically rich graph representation for indoor robotic navigation. Our graph representation encodes: semantic locations such as offices or corridors as nodes, and navigational behaviors such as enter office or cross a corridor as edges. In particular, our navigational behaviors operate directly from visual inputs to produce motor controls and are implemented with deep learning architectures. This enables the robot to avoid explicit computation of its precise location or the geometry of the environment, and enables navigation at a higher level of semantic abstraction. We evaluate the effectiveness of our representation by simulating navigation tasks in a large number of virtual environments. Our results show that using a simple sets of perceptual and navigational behaviors, the proposed approach can successfully guide the way of the robot as it completes navigational missions such as going to a specific office. Furthermore, our implementation shows to be effective to control the selection and switching of behaviors.