# Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases

arXiv: https://arxiv.org/abs/2402.14308 · HTML: https://mlsanity.com/paper/2402.14308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2024.

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

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- [ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models](https://mlsanity.com/paper/2506.07739/) (2025)
- [An RTK-SLAM Dataset for Absolute Accuracy Evaluation in GNSS-Degraded Environments](https://mlsanity.com/paper/2604.07151/) (2026)
- [Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow](https://mlsanity.com/paper/2505.11116/) (2025)
- [UrbanSense:A Framework for Quantitative Analysis of Urban Streetscapes leveraging Vision Large Language Models](https://mlsanity.com/paper/2506.10342/) (2025)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)

## Abstract (arXiv)

We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. Our experimental results on both public and self-collected datasets demonstrate that Ground-Fusion outperforms existing low-cost SLAM systems in corner cases. We release the code and datasets at https://github.com/SJTU-ViSYS/Ground-Fusion.