# SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

arXiv: https://arxiv.org/abs/2209.10817 · HTML: https://mlsanity.com/paper/2209.10817/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [Q-SLAM: Quadric Representations for Monocular SLAM](https://mlsanity.com/paper/2403.08125/) (2024)
- [CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps](https://mlsanity.com/paper/2402.06092/) (2024)
- [LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM](https://mlsanity.com/paper/2602.06991/) (2026)
- [Category-level Meta-learned NeRF Priors for Efficient Object Mapping](https://mlsanity.com/paper/2503.01582/) (2025)
- [A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis](https://mlsanity.com/paper/2605.16779/) (2026)

## Abstract (arXiv)

Object SLAM uses additional semantic information to detect and map objects in the scene, in order to improve the system's perception and map representation capabilities. Quadrics and cubes are often used to represent objects, but their single shape limits the accuracy of object map and thus affects the application of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape parameters into SLAM for representing objects, and propose a separate parameter estimation method that can accurately estimate object pose and adapt to different shapes. Furthermore, we present a lightweight data association strategy for correctly associating semantic observations in multiple views with object landmarks. We implement a monocular semantic SLAM system with real-time performance and conduct comprehensive experiments on public datasets. The results show that our method is able to build accurate object map and has advantages in object representation. Code will be released upon acceptance.