Ground-Fusion：一种对极端情况鲁棒的低成本地面SLAM系统
Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Ground-Fusion是一种面向地面车辆的低成本多传感器融合SLAM系统，通过紧耦合RGB-D、IMU、轮式里程计和GNSS，并采用多策略初始化与异常检测机制，在极端场景下实现优于现有低成本方案的鲁棒定位与建图。
AI中文摘要：
我们提出Ground-Fusion，一种用于地面车辆的低成本传感器融合同时定位与建图（SLAM）系统。该系统具有高效初始化、有效的传感器异常检测与处理、实时稠密彩色建图以及在多样化环境中的鲁棒定位能力。我们在因子图中紧耦合RGB-D图像、惯性测量、轮式里程计和GNSS信号，以实现室内外精确可靠的定位。为确保成功初始化，我们提出一种高效策略，包含三种不同方法：静止、视觉和动态，针对不同情况量身定制。此外，我们开发了检测传感器异常与退化的机制，并对其进行妥善处理以保持系统精度。我们在公开数据集和自采数据集上的实验结果表明，Ground-Fusion在极端情况下优于现有低成本SLAM系统。我们在https://github.com/SJTU-ViSYS/Ground-Fusion发布代码和数据集。
英文摘要：
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.
