# ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology

arXiv: https://arxiv.org/abs/2209.03693 · HTML: https://mlsanity.com/paper/2209.03693/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality](https://mlsanity.com/paper/2503.07819/) (2025)
- [Multimodal LLM Guided Exploration and Active Mapping using Fisher Information](https://mlsanity.com/paper/2410.17422/) (2024)
- [Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building](https://mlsanity.com/paper/2307.05793/) (2023)

## Abstract (arXiv)

Deploying autonomous robots capable of exploring unknown environments has long been a topic of great relevance to the robotics community. In this work, we take a further step in that direction by presenting an open-source active visual SLAM framework that leverages the accuracy of a state-of-the-art graph-SLAM system and takes advantage of the fast utility computation that exploiting the structure of the underlying pose-graph offers. Through careful estimation of a posteriori weighted pose-graphs, D-optimal decision-making is achieved online with the objective of improving localization and mapping uncertainties as exploration occurs.