# Long-Term Localization using Semantic Cues in Floor Plan Maps

arXiv: https://arxiv.org/abs/2210.01456 · HTML: https://mlsanity.com/paper/2210.01456/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2022.

## Built on by (most cited first, 12 of 12)

- [Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems](https://mlsanity.com/paper/2307.05999/) (2023)
- [ObVi-SLAM: Long-Term Object-Visual SLAM](https://mlsanity.com/paper/2309.15268/) (2023)
- [Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap](https://mlsanity.com/paper/2307.12116/) (2023)
- [QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding](https://mlsanity.com/paper/2404.06442/) (2024)
- [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023)
- [ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors](https://mlsanity.com/paper/2512.09065/) (2025)
- [Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization](https://mlsanity.com/paper/2303.10959/) (2023)
- [GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology](https://mlsanity.com/paper/2604.15495/) (2026)
- [VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments](https://mlsanity.com/paper/2605.30506/) (2026)
- [GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization](https://mlsanity.com/paper/2609.08385/) (2026)
- [Multimodal Floorplan Encoding: Learning Dense Modality-Invariant Representations](https://mlsanity.com/paper/2609.12723/) (2026)
- [FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement](https://mlsanity.com/paper/2610.05011/) (2026)

## Abstract (arXiv)

Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.