# Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments

## TL;DR

Pop-up SLAM introduces a real-time monocular plane SLAM method that enhances state estimation and dense mapping in low-texture environments by integrating scene understanding through a novel pop-up 3D plane model.

## Contribution

The paper presents a novel monocular plane SLAM approach using pop-up 3D plane models, improving robustness and accuracy in low-texture environments.

## Key findings

- Achieves 6.2 cm pixel depth error on TUM dataset
- Produces a dense semantic 3D model where existing SLAM fails
- State estimation error of 0.67% on a 60 m dataset

## Abstract

Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.