# TopoNav: Topological Graphs as a Key Enabler for Advanced Object Navigation

arXiv: https://arxiv.org/abs/2509.01364 · HTML: https://mlsanity.com/paper/2509.01364/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2025.

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

- [Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation](https://mlsanity.com/paper/2511.13269/) (2025)
- [MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation](https://mlsanity.com/paper/2602.05467/) (2026)
- [SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Prediction](https://mlsanity.com/paper/2602.21589/) (2026)
- [Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection](https://mlsanity.com/paper/2604.05405/) (2026)
- [SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation](https://mlsanity.com/paper/2608.00527/) (2026)
- [Beyond Spatial Benchmarks: From Spatial Reasoning to Navigation](https://mlsanity.com/paper/2609.29934/) (2026)

## Abstract (arXiv)

Object Navigation (ObjectNav) has made great progress with large language models (LLMs), but still faces challenges in memory management, especially in long-horizon tasks and dynamic scenes. To address this, we propose TopoNav, a new framework that leverages topological structures as spatial memory. By building and updating a topological graph that captures scene connections, adjacency, and semantic meaning, TopoNav helps agents accumulate spatial knowledge over time, retrieve key information, and reason effectively toward distant goals. Our experiments show that TopoNav achieves state-of-the-art performance on benchmark ObjectNav datasets, with higher success rates and more efficient paths. It particularly excels in diverse and complex environments, as it connects temporary visual inputs with lasting spatial understanding.