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gnss/imu localization using kalman filter
| Date | Stars |
|---|---|
| 2026-07-24 | 358 |
| 2026-07-25 | 358 |
| 2026-07-28 | 358 |
| 2026-07-30 | 358 |
| 2026-08-06 | 358 |
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# Kalman Filter Localization
A GNSS/IMU/odometry error-state EKF for ROS 2. It estimates position, velocity,
attitude, and IMU biases.
## Features
- GNSS pose / `NavSatFix` / Doppler velocity
- Gyroscope and accelerometer bias estimation
- GNSS NIS gating and Huber/Cauchy robust losses
- GNSS antenna lever arm and delayed-measurement rewind/replay
- Wheel speed, NHC, ZUPT, and ZIHR
- Continuous-time process noise and second-order discretization
- CSV evaluation and UrbanNav Tokyo ablation tools
## Build and run
```bash
source /opt/ros/$ROS_DISTRO/setup.bash
colcon build --symlink-install
source install/setup.bash
ros2 launch kalman_filter_localization ekf.launch.py
```
Main topics:
| Direction | Topic | Type |
|---|---|---|
| input | `/ekf_localization/initial_pose` | `geometry_msgs/PoseStamped` |
| input | `/gnss_pose` | `geometry_msgs/PoseStamped` |
| input | `/sensing/imu/imu_data` | `sensor_msgs/Imu` |
| input | `/wheel_speed` (optional) | `geometry_msgs/TwistWithCovarianceStamped` |
| output | `/ekf_localization/current_pose` | `geometry_msgs/PoseStamped` |
| output | `/ekf_localization/current_odometry` | `nav_msgs/Odometry` |
See [`ekf.yaml`](kalman_filter_localization_ros2/param/ekf.yaml) for the default
parameters and [`param/profiles`](kalman_filter_localization_ros2/param/profiles) for
dataset-specific configurations.
Select one propagation backend with `propagation_model`:
- `legacy`: frozen historical discretization for regression only.
- `fast`: midpoint nominal integration with second-order `Phi/Qd`.
- `exact`: midpoint integration with Van Loan matrix-exponential `Phi/Qd`, used
as the offline oracle.
The three old propagation booleans are accepted for one compatibility release
only when they are all false (`legacy`) or all true (`fast`). Mixed or
contradictory settings fail node startup.
## GNSS input
`PoseStamped` is the default input. To use `NavSatFix` directly:
```yaml
gnss_input_type: "navsatfix"
gnss_navsatfix_topic: "/gnss/fix"
gnss_navsatfix_use_first_fix_as_origin: true
gnss_navsatfix_use_position_covariance: true
```
Configure the antenna offset with `gnss_lever_arm_{x,y,z}`. Set
`enable_measurement_replay: true` and choose a positive
`measurement_history_duration_sec` to fuse GNSS, Doppler velocity, wheel, and odometry at their
sensor timestamps while publishing the latest IMU state. The replay engine queues measurements up
to `max_future_measurement_wait_sec` ahead of the latest IMU, rejects farther-future or too-old
measurements, and counts duplicate and reverse stamps. With debug topics enabled,
`/ekf_localization/debug/replay_timing`
reports input source, sensor time, arrival time, filter time, and apply time.
`compensate_gnss_delay` is retained only as a deprecated constant-velocity fallback. It is disabled
automatically when replay is enabled. `gnss_time_offset_sec` also shifts the GNSS replay timestamp.
## Evaluation
```bash
ros2 run kalman_filter_localization evaluate_localization \
--estimate-csv result.csv \
--reference-csv ground_truth.csv \
--output-json metrics.json \
--output-csv errors.csv
```
CSV files use `stamp,x,y,z,yaw`. The evaluator reports 3D/horizontal/vertical/yaw APE,
time- and distance-indexed RPE, missing ratio, and optional GNSS-outage endpoint drift,
reacquisition overshoot, and settling time. Alignment, interpolation tolerance, time offset, and
evaluation interval are emitted as a machine-readable policy. `evaluate_consistency` summarizes
NEES/NIS means and chi-square 95% coverage from simulation CSV files.
## UrbanNav Tokyo ablation
The Odaiba u-blox RTK solution contains GNSS outages of up to 81.8 seconds. The
wheel-speed + NHC profile reduced outage 3D RMSE from 657.47 m to 3.15 m.
Convert the official CSV files and RTKLIB solution into a ROS 2 bag:
```bash
ros2 run kalman_filter_localization prepare_Excerpt of 5,323 characters
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