# SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?

arXiv: https://arxiv.org/abs/1709.01500 · HTML: https://mlsanity.com/paper/1709.01500/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

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

- [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://mlsanity.com/paper/2204.00157/) (2022)
- [Perspective from a Higher Dimension: Can 3D Geometric Priors Help Visual Floorplan Localization?](https://mlsanity.com/paper/2507.18881/) (2025)
- [PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model](https://mlsanity.com/paper/2511.09724/) (2025)
- [PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones](https://mlsanity.com/paper/2410.15694/) (2024)
- [Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?](https://mlsanity.com/paper/2508.01216/) (2025)
- [DisCo-FLoc: Semantic-Free Floorplan Localization via SE(2)-Aware Contrastive Disambiguation](https://mlsanity.com/paper/2601.01822/) (2026)
- [Markov Localisation using Heatmap Regression and Deep Convolutional Odometry](https://mlsanity.com/paper/2106.00371/) (2021)
- [Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization](https://mlsanity.com/paper/2303.10959/) (2023)

## Abstract (arXiv)

How does a person work out their location using a floorplan? It is probably safe to say that we do not explicitly measure depths to every visible surface and try to match them against different pose estimates in the floorplan. And yet, this is exactly how most robotic scan-matching algorithms operate. Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world. And yet, this is how most vision-based approaches localise. Humans do the exact opposite. Instead of depth, we use high level semantic cues. Instead of extruding the floorplan up into the third dimension, we collapse the 3D world into a 2D representation. Evidence of this is that many of the floorplans we use in everyday life are not accurate, opting instead for high levels of discriminative landmarks. In this work, we use this insight to present a global localisation approach that relies solely on the semantic labels present in the floorplan and extracted from RGB images. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.