# Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments

arXiv: https://arxiv.org/abs/2004.05303 · HTML: https://mlsanity.com/paper/2004.05303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

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

- [SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints](https://mlsanity.com/paper/2109.04884/) (2021)
- [Perspective-1-Ellipsoid: Formulation, Analysis and Solutions of the Camera Pose Estimation Problem from One Ellipse-Ellipsoid Correspondence](https://mlsanity.com/paper/2208.12513/) (2022)
- [Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment](https://mlsanity.com/paper/2110.08977/) (2021)
- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)
- [Stable and Scalable Bundle Adjustment of Holistic 3D Structures](https://mlsanity.com/paper/2609.04026/) (2026)

## Abstract (arXiv)

Aiming at the application environment of indoor mobile robots, this paper proposes a sparse object-level SLAM algorithm based on an RGB-D camera. A quadric representation is used as a landmark to compactly model objects, including their position, orientation, and occupied space. The state-of-art quadric-based SLAM algorithm faces the observability problem caused by the limited perspective under the plane trajectory of the mobile robot. To solve the problem, the proposed algorithm fuses both object detection and point cloud data to estimate the quadric parameters. It finishes the quadric initialization based on a single frame of RGB-D data, which significantly reduces the requirements for perspective changes. As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters. Experiments have shown that compared with the state-of-art algorithm, especially on the forward trajectory of mobile robots, the proposed algorithm significantly improves the accuracy and convergence speed of quadric reconstruction. Finally, we made available an opensource implementation to replicate the experiments.