# Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

arXiv: https://arxiv.org/abs/1709.05533 · HTML: https://mlsanity.com/paper/1709.05533/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2017.

## Built on by (most cited first, 13 of 13)

- [Multiview Scene Graph](https://mlsanity.com/paper/2410.11187/) (2024)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)
- [TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments](https://mlsanity.com/paper/2402.04061/) (2024)
- [CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction](https://mlsanity.com/paper/2410.01273/) (2024)
- [FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation](https://mlsanity.com/paper/2607.08359/) (2026)
- [Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond](https://mlsanity.com/paper/2311.04472/) (2023)
- [PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching](https://mlsanity.com/paper/2404.01674/) (2024)
- [Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments](https://mlsanity.com/paper/2506.00083/) (2025)
- [OpenBot-Fleet: A System for Collective Learning with Real Robots](https://mlsanity.com/paper/2405.07515/) (2024)
- [NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot](https://mlsanity.com/paper/2410.11492/) (2024)
- [TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing](https://mlsanity.com/paper/2510.04100/) (2025)
- [Loop Closure using AnyLoc Visual Place Recognition in DPV-SLAM](https://mlsanity.com/paper/2601.02723/) (2026)

## Abstract (arXiv)

Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies the navigation task by providing a map to the robot which is tailored for path planning use. This novel approach transforms a sparse feature-based map from a visual Simultaneous Localization And Mapping (SLAM) system into a three-dimensional topological map. This is done in two steps. First, we extract occupancy information directly from the noisy sparse point cloud. Then, we create a set of convex free-space clusters, which are the vertices of the topological map. We show that this representation improves the efficiency of global planning, and we provide a complete derivation of our algorithm. Planning experiments on real world datasets demonstrate that we achieve similar performance as RRT* with significantly lower computation times and storage requirements. Finally, we test our algorithm on a mobile robotic platform to prove its advantages.