# FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization

arXiv: https://arxiv.org/abs/2203.00292 · HTML: https://mlsanity.com/paper/2203.00292/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment](https://mlsanity.com/paper/2012.08228/) (2020)

## Built on by (most cited first, 3 of 3)

- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones](https://mlsanity.com/paper/2410.15694/) (2024)
- [FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement](https://mlsanity.com/paper/2610.05011/) (2026)

## Abstract (arXiv)

We present a novel framework for floor plan-based, full six degree-of-freedom LiDAR localization. Our approach relies on robust ceiling and ground plane detection, which solves part of the pose and supports the segmentation of vertical structure elements such as walls and pillars. Our core contribution is a novel nearest neighbour data structure for an efficient look-up of nearest vertical structure elements from the floor plan. The registration is realized as a pair-wise regularized windowed pose graph optimization. Highly efficient, accurate and drift-free long-term localization is demonstrated on multiple scenes.