# Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs

arXiv: https://arxiv.org/abs/2311.12245 · HTML: https://mlsanity.com/paper/2311.12245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2023.

## Built on by (most cited first, 7 of 7)

- [SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks](https://mlsanity.com/paper/2403.19474/) (2024)
- [SG-Reg: Generalizable and Efficient Scene Graph Registration](https://mlsanity.com/paper/2504.14440/) (2025)
- [VGGT-SLAM++](https://mlsanity.com/paper/2604.06830/) (2026)
- [Context-Based Visual-Language Place Recognition](https://mlsanity.com/paper/2410.19341/) (2024)
- [SymboSLAM: Semantic Map Generation in a Multi-Agent System](https://mlsanity.com/paper/2403.15504/) (2024)
- [Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping](https://mlsanity.com/paper/2405.01398/) (2024)
- [Visual Loop Closure Detection Through Deep Graph Consensus](https://mlsanity.com/paper/2505.21754/) (2025)

## Abstract (arXiv)

Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This paper introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.