Open-source software for precise vehicle localization using GNSS and IMU data fusion.
Eagleye is an open-source software for precise vehicle localization that fuses GNSS and IMU data. It solves the problem of achieving lane-level positioning in urban environments by combining satellite measurements with inertial sensors, providing stable and accurate position and orientation estimates even when GNSS signals are degraded.
Researchers, engineers, and developers working on autonomous vehicles, robotics, and advanced driver-assistance systems (ADAS) who need reliable, low-cost localization solutions.
Developers choose Eagleye for its research-backed algorithms, ROS 2 compatibility, and focus on optimizing long time-series data to deliver high-precision localization without relying on expensive proprietary systems.
Precise localization based on GNSS and IMU.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Based on multiple academic papers from Meijo University, utilizing GNSS Doppler measurements for lane-level positioning in urban areas, as cited in the README.
Packaged as ROS 2 nodes with standard topic interfaces, allowing seamless incorporation into robotic and automotive systems without custom middleware.
Compatible with RTK-capable GNSS receivers like Septentrio Mosaic, enabling centimeter-level accuracy for precise localization.
Works with various IMUs and GNSS receivers, though specific models are recommended, and parameters can be adjusted in YAML files for customization.
Requires cloning and building multiple dependencies like RTKLIB forks, configuring sensor-specific parameters, and handling coordinate system adjustments, which is time-consuming.
As noted in the sample run, estimation outputs require about 100 seconds of data accumulation, limiting usability for applications needing instant localization.
Marked as an alpha version, indicating potential instability, breaking changes, and incomplete features that may affect production use.
Primarily focuses on GNSS and IMU fusion; integration with other sensors like LIDAR or cameras is not a core feature, requiring additional packages or custom work.