https://github.com/ethz-asl/rio
Graph-based, sparse radar-inertial odometry estimation
https://github.com/ethz-asl/rio
Last synced: 9 months ago
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Graph-based, sparse radar-inertial odometry estimation
- Host: GitHub
- URL: https://github.com/ethz-asl/rio
- Owner: ethz-asl
- License: bsd-3-clause
- Created: 2024-04-02T19:49:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-23T22:52:12.000Z (11 months ago)
- Last Synced: 2025-03-29T23:07:40.028Z (9 months ago)
- Language: C++
- Size: 7.63 MB
- Stars: 120
- Watchers: 10
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# rio
Graph-based, sparse radar-inertial odometry m-estimation with barometer support and zero-velocity tracking.
**Paper**: https://arxiv.org/pdf/2408.05764
```
@inproceedings{girod2024brio,
author = {Rik Girod and Marco Hauswirth and Patrick Pfreundschuh and Mariano Biasio and Roland Siegwart},
title = {A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests},
booktitle={IEEE Int. Conf. Multisensor Fusion Integration Intell. Syst.},
year={2024}
}
```
# Installation
Install [ROS noetic](https://wiki.ros.org/noetic/Installation/Ubuntu).
```
sudo apt install git build-essential python3-rosdep python3-catkin-tools ros-noetic-rqt-multiplot -y
mkdir catkin_ws
cd catkin_ws
mkdir src
catkin init
cd src
git clone https://github.com/ethz-asl/rio.git
git clone https://github.com/ethz-asl/lpp.git
git clone https://github.com/rikba/gtsam_catkin.git
cd ..
rosdep install --from-paths src --ignore-src -r -y
catkin build
```
# Launch
Start RIO in one terminal
```
source ~/catkin_ws/devel/setup.bash
roslaunch rio rio.launch visualize:=true
```
Download an example dataset sequence.
Replay the bag
```
rosbag play 01_urban_night_H_raw.bag
```
# Supported sensors
| Sensor | Default topic | Message type | Required | Note |
| ------------ | ---------------------- | ------------------------- | -------- | --------------------------------- |
| IMU | /imu/data_raw | sensor_msgs/Imu | Yes | Calibrate gyro turn-on-bias! |
| IMU filtered | /imu/data | sensor_msgs/Imu | Yes | for initialization |
| Radar | /radar/cfar_detections | sensor_msgs/PointCloud2 | Yes | |
| Barometer | /baro/pressure | sensor_msgs/FluidPressure | No | Activate in [cfg](./cfg/rio.yaml) |
Radar point cloud format, see also [mav_sensors_ros](https://github.com/ethz-asl/mav_sensors_ros/blob/main/src/radar.cpp#L146-L236).
```
| Field name | Size |
| ---------- | ------- |
| x | FLOAT32 |
| y | FLOAT32 |
| z | FLOAT32 |
| doppler | FLOAT32 |
| snr | INT16 |
| noise | INT16 |
```
# Dataset
The [dataset](https://doi.org/10.3929/ethz-b-000713420) contains 15 sequences of urban night, forest path, field, and deep forest handheld and flown data. It can be downloaded with and without camera images.
[Video of closed-loop quadrotor control in city and forest.](https://github.com/ethz-asl/rio/assets/11293852/7bc95fa8-6fa1-4172-ad63-5ae1d3a38d58)
| Scenario | No. | Link w/o camera | Link with camera |
| ----------- | --- | --------------- | ---------------- |
| Urban Night | 01 | [01_urban_night_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=01_urban_night_H_raw_no_img.bag) | [01_urban_night_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=01_urban_night_H_raw.bag) |
| | 02 | [02_urban_night_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=02_urban_night_H_raw_no_img.bag) | [02_urban_night_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=02_urban_night_H_raw.bag) |
| | 03 | [03_urban_night_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=03_urban_night_H_raw_no_img.bag) | [03_urban_night_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=03_urban_night_H_raw.bag) |
| | 04 | [04_urban_night_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=04_urban_night_H_raw_no_img.bag) | [04_urban_night_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=04_urban_night_H_raw.bag) |
| | 05 | [05_urban_night_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=05_urban_night_F_raw_no_img.bag) | [05_urban_night_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=05_urban_night_F_raw.bag) |
| | 06 | [06_urban_night_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=06_urban_night_F_raw_no_img.bag) | [06_urban_night_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=06_urban_night_F_raw.bag) |
| | 07 | [07_urban_night_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=07_urban_night_F_raw_no_img.bag) | [07_urban_night_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=07_urban_night_F_raw.bag) |
| Forest Path | 08 | [08_forest_path_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=08_forest_path_H_raw_no_img.bag) | [08_forest_path_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=08_forest_path_H_raw.bag) |
| | 09 | [09_forest_path_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=09_forest_path_H_raw_no_img.bag) | [09_forest_path_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=09_forest_path_H_raw.bag) |
| | 10 | [10_forest_path_H_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=10_forest_path_H_raw_no_img.bag) | [10_forest_path_H_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=10_forest_path_H_raw.bag) |
| | 11 | [11_forest_path_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=11_forest_path_F_raw_no_img.bag) | [11_forest_path_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=11_forest_path_F_raw.bag) |
| | 12 | [12_forest_path_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=12_forest_path_F_raw_no_img.bag) | [12_forest_path_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=12_forest_path_F_raw.bag) |
| Flat Field | 13 | [13_flat_field_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=13_flat_field_F_raw_no_img.bag) | [13_flat_field_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=13_flat_field_F_raw.bag) |
| | 14 | [14_flat_field_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=14_flat_field_F_raw_no_img.bag) | [14_flat_field_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=14_flat_field_F_raw.bag) |
| Tree Slalom | 15 | [15_tree_slalom_F_raw_no_img.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=15_tree_slalom_F_raw_no_img.bag) | [15_tree_slalom_F_raw.bag](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=15_tree_slalom_F_raw.bag) |
| | | |
| All | 01-15 | [raw_no_img_01-15.zip (200MB)](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download?path=%2F&files=raw_no_img_01-15.zip) | [all](https://libdrive.ethz.ch/index.php/s/wfmZSbsx4x7yFPA/download) |
## Calibration
```
| Field name | IMU to radar | radar to cam |
| ---------- | ------------ | ------------ |
| x | 0.122 | -0.020 |
| y | 0.000 | -0.015 |
| z | -0.025 | 0.000 |
| qx | 0.67620958 | 0.000 |
| qy | 0.67620958 | 0.7071068 |
| qz | -0.20673802 | 0.7071068 |
| qw | -0.20673802 | 0.000 |
```
```
```
# Calibration Optimization (Experimental)
[rio_calibration_node.cpp](https://github.com/ethz-asl/rio/blob/main/src/rio_calibration_node.cpp) allows you to calibrate the rigid transformation between IMU and radar.
The calibration procedure:
1. Move the device in a loop, recording RIO odometry output (`/rio/odometry_optimize`), IMU (`/imu/data_raw`) and radar (`/radar/cfar_detections`). The device has to be located in the same position and orientation at start and goal to make the [loop closure](https://github.com/ethz-asl/rio/blob/main/cfg/calibration.yaml#L10-L12).
2. Set the initial guess in [calibration.yaml](https://github.com/ethz-asl/rio/blob/main/cfg/calibration.yaml#L6-L7).
3. `roslaunch rio calibration.launch`
This should refine your initial guess. Note you need to excite roll and pitch in your dataset. I noticed that you could also relax the [gyro bias process noise](https://github.com/ethz-asl/rio/blob/main/cfg/rio.yaml#L8), as it is well observable through loop closure.
# Related packages
| Package | Description | Link |
| --------------- | ------------------------------- | -------------------------------------------------------------- |
| mav_sensors | Linux user space sensor drivers | [mav_sensors](https://github.com/ethz-asl/mav_sensors) |
| mav_sensors_ros | ROS sensor interface | [mav_sensors_ros](https://github.com/ethz-asl/mav_sensors_ros) |