How to set up Eagleye with a custom IMU not listed in the recommendations?

Ensure your IMU outputs data on the /imu/data_raw ROS topic with the correct coordinate system. Adjust the roll parameter in sensors_tf.yaml to match your sensor's orientation relative to the vehicle, as described in the IMU settings section.

Eagleye vs robot_localization for ROS 2: which is better for GNSS fusion?

Eagleye is specialized for GNSS/IMU fusion with Doppler-based positioning, ideal for lane-level accuracy in urban areas. robot_localization is more general-purpose for fusing various sensors but may not offer the same precision for GNSS-heavy applications without customization.

What's the best way to visualize Eagleye output data on a map?

Use the provided tools like eagleye_fix2kml to convert /eagleye/fix topics to KML format for mapping. Sample ROS bags and integration with Autoware maps are also available for testing and visualization.

How to reduce the 100-second wait time in Eagleye for real-time use?

The delay is inherent to the algorithm's optimization of long time-series data. Adjusting parameters in eagleye_config.yaml might help, but it's a trade-off for accuracy, and full elimination may require algorithmic modifications.

Does Eagleye support integration with LIDAR for SLAM in autonomous driving?

Eagleye focuses on GNSS/IMU fusion; for LIDAR integration, you would need to combine it with other packages like ndt_scan_matcher. The sample data includes LIDAR points, but fusion is not built-in, requiring external systems.

Troubleshooting RTKLIB connection issues in Eagleye setup

Ensure RTKLIB is built from the MapIV fork and running correctly. Check UDP settings in nmea_ros_bridge config and verify GNSS receiver output using the 'status 0.1' command, as outlined in the real-time operation steps.

Open-Awesome

eagleye

BSD-3-ClauseC++v1.7.3-ros2

Open-source software for precise vehicle localization using GNSS and IMU data fusion.

GitHub

769 stars170 forks0 contributors

What is eagleye?

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.

Target Audience

Researchers, engineers, and developers working on autonomous vehicles, robotics, and advanced driver-assistance systems (ADAS) who need reliable, low-cost localization solutions.

Value Proposition

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.

Overview

Precise localization based on GNSS and IMU.

Use Cases

Best For

Achieving lane-level vehicle positioning in urban areas
Integrating GNSS and IMU data for robotic navigation
Building low-cost localization systems for autonomous driving research
Enhancing ADAS with precise real-time location data
Fusing sensor data in ROS 2-based automotive applications
Conducting research on sensor fusion algorithms for localization

Not Ideal For

Applications requiring immediate localization on startup without initialization delays
Projects not built on ROS 2 or needing a standalone, framework-agnostic library
Teams without access to specific, recommended GNSS receivers like Septentrio Mosaic
Systems that require fusion with additional sensors like cameras or LIDAR for holistic perception

Pros & Cons

Pros

Research-Backed Accuracy

Based on multiple academic papers from Meijo University, utilizing GNSS Doppler measurements for lane-level positioning in urban areas, as cited in the README.

ROS 2 Native Integration

Packaged as ROS 2 nodes with standard topic interfaces, allowing seamless incorporation into robotic and automotive systems without custom middleware.

Real-Time Kinematic Support

Compatible with RTK-capable GNSS receivers like Septentrio Mosaic, enabling centimeter-level accuracy for precise localization.

Sensor Configuration Flexibility

Works with various IMUs and GNSS receivers, though specific models are recommended, and parameters can be adjusted in YAML files for customization.

Cons

Complex Installation and Setup

Requires cloning and building multiple dependencies like RTKLIB forks, configuring sensor-specific parameters, and handling coordinate system adjustments, which is time-consuming.

Significant Initialization Delay

As noted in the sample run, estimation outputs require about 100 seconds of data accumulation, limiting usability for applications needing instant localization.

Alpha Version Limitations

Marked as an alpha version, indicating potential instability, breaking changes, and incomplete features that may affect production use.

Limited Multi-Sensor Fusion

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.

Frequently Asked Questions

Related Projects

Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

Stars19,148

Forks4,502

Last commit1 year ago

evo

Python package for the evaluation of odometry and SLAM

Stars4,285

Forks792

Last commit6 days ago

PROJ

PROJ - Cartographic Projections and Coordinate Transformations Library

robot_localization is a package of nonlinear state estimation nodes. The package was developed by Charles River Analytics, Inc. Please ask questions on answers.ros.org.

Stars1,934

Forks1,026

Last commit8 days ago

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub