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.

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

Radar point cloud format, see also mav_sensors_ros.

| Field name | Size    |
| ---------- | ------- |
| x          | FLOAT32 |
| y          | FLOAT32 |
| z          | FLOAT32 |
| doppler    | FLOAT32 |
| snr        | INT16   |
| noise      | INT16   |

Dataset

The dataset 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.

dataset_quadrotor_examples.mp4

Scenario	No.	Link w/o camera
Urban Night	01	01_urban_night_H_raw_no_img.bag
	02	02_urban_night_H_raw_no_img.bag
	03	03_urban_night_H_raw_no_img.bag
	04	04_urban_night_H_raw_no_img.bag
	05	05_urban_night_F_raw_no_img.bag
	06	06_urban_night_F_raw_no_img.bag
	07	07_urban_night_F_raw_no_img.bag
Forest Path	08	08_forest_path_H_raw_no_img.bag
	09	09_forest_path_H_raw_no_img.bag
	10	10_forest_path_H_raw_no_img.bag
	11	11_forest_path_F_raw_no_img.bag
	12	12_forest_path_F_raw_no_img.bag
Flat Field	13	13_flat_field_F_raw_no_img.bag
	14	14_flat_field_F_raw_no_img.bag
Tree Slalom	15	15_tree_slalom_F_raw_no_img.bag
All	01-15	all_raw_no_img

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        |

<node name="imu_to_radar_broadcaster" pkg="tf2_ros" type="static_transform_publisher" args="0.122 0.000 -0.025 0.67620958 0.67620958 -0.20673802 -0.20673802 'bmi088' 'awr1843aop'" />
<node name="radar_to_cam_broadcaster" pkg="tf2_ros" type="static_transform_publisher" args="-0.020 -0.015 0.0 0.0 0.7071068 0.7071068 0.0 'awr1843aop' 'cam'" />

Calibration Optimization (Experimental)

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.
2. Set the initial guess in calibration.yaml.
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, as it is well observable through loop closure.

Related packages

Package	Description	Link
mav_sensors	Linux user space sensor drivers	mav_sensors
mav_sensors_ros	ROS sensor interface	mav_sensors_ros