Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases
CoRR(2024)
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要