# Robot Navigation in Unseen Spaces using an Abstract Map

arXiv: https://arxiv.org/abs/2001.11684 · HTML: https://mlsanity.com/paper/2001.11684/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Learning and Executing Re-usable Behaviour Trees from Natural Language Instruction](https://mlsanity.com/paper/2106.01650/) (2021)
- [Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching](https://mlsanity.com/paper/2201.05977/) (2022)
- [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)

## Abstract (arXiv)

Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.