# SymboSLAM: Semantic Map Generation in a Multi-Agent System

arXiv: https://arxiv.org/abs/2403.15504 · HTML: https://mlsanity.com/paper/2403.15504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics](https://mlsanity.com/paper/1804.05526/) (2018)
- [DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams](https://mlsanity.com/paper/1909.12198/) (2019)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs](https://mlsanity.com/paper/2311.12245/) (2023)
- [Robot Navigation in Unseen Spaces using an Abstract Map](https://mlsanity.com/paper/2001.11684/) (2020)
- [Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming](https://mlsanity.com/paper/2203.12955/) (2022)
- [Efficient quantitative assessment of robot swarms: coverage and targeting L'evy strategies](https://mlsanity.com/paper/2202.06931/) (2022)

## Abstract (arXiv)

Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.