# Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching

arXiv: https://arxiv.org/abs/2201.05977 · HTML: https://mlsanity.com/paper/2201.05977/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 16 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment](https://mlsanity.com/paper/2010.09297/) (2020)
- [Robot Navigation in Unseen Spaces using an Abstract Map](https://mlsanity.com/paper/2001.11684/) (2020)

## Built on by (most cited first, 2 of 2)

- [Endowing Embodied Agents with Spatial Reasoning Capabilities for Vision-and-Language Navigation](https://mlsanity.com/paper/2504.08806/) (2025)
- [SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas](https://mlsanity.com/paper/2510.19766/) (2025)

## Abstract (arXiv)

Mapping and localization are two essential tasks for mobile robots in real-world applications. However, largescale and dynamic scenes challenge the accuracy and robustness of most current mature solutions. This situation becomes even worse when computational resources are limited. In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness. Different from previous methods, our method does not need a prior constructed precise geometric map, which greatly releases the storage burden, especially for large-scale navigation. We use object-level features with both semantic and geometric information to model landmarks in the environment. Particularly, a learning topological primitive is first proposed to efficiently obtain and organize the object-level landmarks. On the basis of this, we use a robot-centric mapping framework to represent the environment as a semantic topology graph and relax the burden of maintaining global consistency at the same time. Besides, a hierarchical memory management mechanism is introduced to improve the efficiency of online mapping with limited computational resources. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor, and performing multi-constraint graph matching to compare scene similarity. Finally, we test our method on a low-cost embedded platform to demonstrate its advantages. Experimental results on a large scale and multi-session real-world environment show that the proposed method outperforms the state of arts in terms of lightweight and robustness.