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.

翻译：本文提出Ground-Fusion，一种面向地面车辆的低成本传感器融合同步定位与建图系统。该系统具备高效初始化、传感器异常检测与处理、实时稠密彩色建图以及在多种环境下鲁棒定位的能力。我们通过因子图紧密融合RGB-D图像、惯性测量、轮式里程计与GNSS信号，实现室内外场景下的精确可靠定位。为保障初始化成功，我们提出包含静态、视觉与动态三种策略的高效方案，以适配不同工况。此外，我们开发了传感器异常与性能退化检测机制，并通过自适应处理维持系统精度。在公开数据集与自采数据集上的实验表明，Ground-Fusion在极端工况下优于现有低成本SLAM系统。代码与数据集已开源至https://github.com/SJTU-ViSYS/Ground-Fusion。