# Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network

arXiv: https://arxiv.org/abs/1903.01804 · HTML: https://mlsanity.com/paper/1903.01804/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

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

- [Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds](https://mlsanity.com/paper/2111.14843/) (2021)
- [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://mlsanity.com/paper/2104.09169/) (2021)
- [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://mlsanity.com/paper/2204.00157/) (2022)
- [Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2109.15286/) (2021)
- [Pay Self-Attention to Audio-Visual Navigation](https://mlsanity.com/paper/2210.01353/) (2022)
- [F3Loc: Fusion and Filtering for Floorplan Localization](https://mlsanity.com/paper/2403.03370/) (2024)
- [Large-Scale Person Detection and Localization using Overhead Fisheye Cameras](https://mlsanity.com/paper/2307.08252/) (2023)
- [Supercharging Floorplan Localization with Semantic Rays](https://mlsanity.com/paper/2507.09291/) (2025)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation](https://mlsanity.com/paper/2112.06180/) (2021)
- [UnLoc: Leveraging Depth Uncertainties for Floorplan Localization](https://mlsanity.com/paper/2509.11301/) (2025)
- [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://mlsanity.com/paper/2210.12935/) (2022)
- [Perspective from a Higher Dimension: Can 3D Geometric Priors Help Visual Floorplan Localization?](https://mlsanity.com/paper/2507.18881/) (2025)
- [FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization](https://mlsanity.com/paper/2203.00292/) (2022)
- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [Visual Place Representation and Recognition from Depth Images](https://mlsanity.com/paper/2112.13707/) (2021)
- [Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?](https://mlsanity.com/paper/2508.01216/) (2025)
- [Multi-Perspective Anomaly Detection](https://mlsanity.com/paper/2105.09903/) (2021)
- [Active Semantic Localization with Graph Neural Embedding](https://mlsanity.com/paper/2305.06141/) (2023)
- [BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement](https://mlsanity.com/paper/2412.03434/) (2024)
- [DisCo-FLoc: Semantic-Free Floorplan Localization via SE(2)-Aware Contrastive Disambiguation](https://mlsanity.com/paper/2601.01822/) (2026)
- [SceneAligner: 3D-Grounded Floorplan Localization in the Wild](https://mlsanity.com/paper/2605.22581/) (2026)
- [Offset-Guided Attention Network for Room-Level Aware Floor Plan Segmentation](https://mlsanity.com/paper/2210.17411/) (2022)
- [Semi-Supervised 360 Layout Estimation with Panoramic Collaborative Perturbations](https://mlsanity.com/paper/2503.01114/) (2025)
- [SkeNa: Learning to Navigate Unseen Environments Based on Abstract Hand-Drawn Maps](https://mlsanity.com/paper/2508.03053/) (2025)
- [GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization](https://mlsanity.com/paper/2609.08385/) (2026)
- [ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning](https://mlsanity.com/paper/2610.17178/) (2026)

## Abstract (arXiv)

Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed, the majority of them relies on maps generated from data gathered with the same sensor modality used for localization. Typically, tedious labor by experts is needed to acquire this data, thus limiting the readiness of the system as well as its ease of installation for inexperienced operators. In this paper, we propose a memory and computationally efficient monocular camera-based localization system that allows a robot to estimate its pose given an architectural floor plan. Our method employs a convolutional neural network to predict room layout edges from a single camera image and estimates the robot pose using a particle filter that matches the extracted edges to the given floor plan. We evaluate our localization system using multiple real-world experiments and demonstrate that it has the robustness and accuracy required for reliable indoor navigation.