# OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS

arXiv: https://arxiv.org/abs/2510.04612 · HTML: https://mlsanity.com/paper/2510.04612/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [Understanding Sensor Vulnerabilities in Industrial XR Tracking](https://mlsanity.com/paper/2602.14413/) (2026)
- [DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM](https://mlsanity.com/paper/2607.01757/) (2026)
- [AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels](https://mlsanity.com/paper/2609.02798/) (2026)
- [VI3: Grounding Pretrained 3D Foundation Models with Inertial Cues](https://mlsanity.com/paper/2609.03824/) (2026)

## Abstract (arXiv)

To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localization and Mapping (SLAM) system building dense volumetric occupancy maps, while scalable to large environments and operating in realtime. Our unified SLAM framework seamlessly integrates different sensor modalities: visual, inertial, measured or learned depth, LiDAR and Global Navigation Satellite System (GNSS) measurements. Unlike most state-of-the-art SLAM systems, we advocate using dense volumetric map representations when leveraging depth or range-sensing capabilities. We employ an efficient submapping strategy that allows our system to scale to large environments, showcased in sequences of up to 9 kilometers. OKVIS2-X enhances its accuracy and robustness by tightly-coupling the estimator and submaps through map alignment factors. Our system provides globally consistent maps, directly usable for autonomous navigation. To further improve the accuracy of OKVIS2-X, we also incorporate the option of performing online calibration of camera extrinsics. Our system achieves the highest trajectory accuracy in EuRoC against state-of-the-art alternatives, outperforms all competitors in the Hilti22 VI-only benchmark, while also proving competitive in the LiDAR version, and showcases state of the art accuracy in the diverse and large-scale sequences from the VBR dataset.