Summary
FusionCore has already removed much of the tuning burden traditionally associated with robot localization. A natural next step would be reducing setup and configuration requirements as well.
The long-term goal is simple:
ros2 launch fusioncore auto.launch.py
and have FusionCore automatically discover, classify, validate, and fuse available sensors with little to no user configuration.
Motivation
Most localization problems are not caused by the filter itself. They are caused by:
- Incorrect TF trees
- Sensor configuration mistakes
- Poor covariance values
- Timestamp issues
- Misidentified or duplicate data sources
Users often spend more time configuring and debugging their localization stack than actually tuning the estimator.
FusionCore is already moving toward self-tuning and adaptive estimation. Extending that philosophy to setup and system integration could significantly improve the user experience.
Proposed Features
Sensor Auto-Discovery
Automatically detect and classify available sensors:
- IMU
- Wheel odometry
- Visual odometry
- LiDAR odometry
- GPS / GNSS
Example:
Found:
✓ IMU: /imu/data
✓ Wheel Odometry: /wheel/odom
✓ GPS: /fix
✓ Visual Odometry: /vslam/odom
Automatic Noise Characterization
Instead of requiring users to provide sensor noise values, estimate them directly from incoming data.
Example workflow:
Collecting stationary IMU data...
Estimating gyro noise...
Estimating accelerometer noise...
Calibration complete.
This could further reduce the amount of sensor-specific configuration required.
TF Validation
Detect common frame and mounting issues before fusion begins.
Examples:
- IMU mounted upside down
- Suspicious frame rotations
- Missing transforms
- Invalid TF chains
Example warning:
Warning: imu_link appears rotated 180° relative to base_link.
Sensor Quality and Health Monitoring
Provide visibility into sensor reliability and confidence.
Example:
GPS Quality: Excellent
IMU Quality: Good
Wheel Odometry: Slip detected
This would help users diagnose issues much faster than manually inspecting topics and covariances.
Future Vision
The end goal is not just a better filter.
The goal is a localization system that understands the robot, configures itself, validates its inputs, and provides useful feedback when something is wrong.
FusionCore's biggest differentiator should not be that users spend less time tuning parameters.
It should be that users spend less time thinking about localization at all.
Summary
FusionCore has already removed much of the tuning burden traditionally associated with robot localization. A natural next step would be reducing setup and configuration requirements as well.
The long-term goal is simple:
and have FusionCore automatically discover, classify, validate, and fuse available sensors with little to no user configuration.
Motivation
Most localization problems are not caused by the filter itself. They are caused by:
Users often spend more time configuring and debugging their localization stack than actually tuning the estimator.
FusionCore is already moving toward self-tuning and adaptive estimation. Extending that philosophy to setup and system integration could significantly improve the user experience.
Proposed Features
Sensor Auto-Discovery
Automatically detect and classify available sensors:
Example:
Automatic Noise Characterization
Instead of requiring users to provide sensor noise values, estimate them directly from incoming data.
Example workflow:
This could further reduce the amount of sensor-specific configuration required.
TF Validation
Detect common frame and mounting issues before fusion begins.
Examples:
Example warning:
Sensor Quality and Health Monitoring
Provide visibility into sensor reliability and confidence.
Example:
This would help users diagnose issues much faster than manually inspecting topics and covariances.
Future Vision
The end goal is not just a better filter.
The goal is a localization system that understands the robot, configures itself, validates its inputs, and provides useful feedback when something is wrong.
FusionCore's biggest differentiator should not be that users spend less time tuning parameters.
It should be that users spend less time thinking about localization at all.