# VIR-SLAM: Visual, Inertial, and Ranging SLAM for single and multi-robot systems

arXiv: https://arxiv.org/abs/2006.00420 · HTML: https://mlsanity.com/paper/2006.00420/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

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

- [Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems](https://mlsanity.com/paper/2301.06230/) (2023)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [Gaussian Variational Inference with Non-Gaussian Factors for State Estimation: A UWB Localization Case Study](https://mlsanity.com/paper/2512.19855/) (2025)

## Abstract (arXiv)

Monocular cameras coupled with inertial measurements generally give high performance visual inertial odometry. However, drift can be significant with long trajectories, especially when the environment is visually challenging. In this paper, we propose a system that leverages ultra-wideband ranging with one static anchor placed in the environment to correct the accumulated error whenever the anchor is visible. We also use this setup for collaborative SLAM: different robots use mutual ranging (when available) and the common anchor to estimate the transformation between each other, facilitating map fusion Our system consists of two modules: a double layer ranging, visual, and inertial odometry for single robots, and a transformation estimation module for collaborative SLAM. We test our system on public datasets by simulating an ultra-wideband sensor as well as on real robots. Experiments show our method can outperform state-of-the-art visual-inertial odometry by more than 20%. For visually challenging environments, our method works even the visual-inertial odometry has significant drift Furthermore, we can compute the collaborative SLAM transformation matrix at almost no extra computation cost.