mulran2bag

Pure command line tool that converts MulRan datasets to bag files. This implementation is based on file_player_mulran.

Requirements:

1. Download MulRan datasets: https://sites.google.com/view/mulran-pr/dataset
2. Order the downloaded data: https://www.youtube.com/watch?v=uU-FC-GmHXA

After completing these steps, the root folder should contain a global_pose.csv and a sensor_data folder. The path to the root folder can be passed to the mulran2bag-Node:

rosrun mulran2bag mulran2bag path/to/mulran/root_folder

to convert it to a bag-file named root_folder.bag.

Data

Sensor	Topic	Message
Ouster OS1-64 LiDAR	os1_points	sensor_msgs/PointCloud2
Navtech Radar CIR204-H	Navtech/Polar	sensor_msgs/Image
Xsens IMU MTi-300 *	ìmu/data_raw	sensor_msgs/Imu
	imu/mag	sensor_msgs/MagneticField
GPS U-Blox EVK-7P *	gps/fix	sensor_msgs/NavSatFix

* If it is the same as "Complex urban dataset ...", 2019 IRJJ, Jinyong Jeong

Transformations

Dynamic Transformations

The ground truth is used to generate a dynamic transformation from base_link to gt. Since the gt frame is added below base_link you can start another localization without destroying the tf tree.

Static Transformations

All static transformations are generated exactly as a static transform broadcaster would do it and are written to the tf_static-topic.

Currently implemented static transformations:

• base_link to ouster: trans = (1.7042 m, -0.021 m, 1.8047 m), rot = (0.0001°, 0.0003°, 179.6654°)
• base_link to radar_polar: trans = (1.50 m, -0.04 m, 1.97 m), rot = (0°, 0°, 0.9°)
• base_link to imu: trans = (-0.07 m, 0.0 m, 1.7 m), rot = (0°, 0°, 0°)
• base_link to gps: trans = (-0.32 m, 0.0 m, 1.7 m), rot = (0°, 0°, 0°)

Figured out, file_player_mulran referred to another paper "Complex urban dataset with multi-level sensors from highly diverse urban environments", 2019 IRJJ, Jinyong Jeong, for exterinsic calibration of everything that is not mentioned. Maybe there are some calibration files hidden in this dataset. However, the link to the dataset https://irap.kaist.ac.kr/dataset/ is dead (Sep 6, 2023). So I tried to estimate the missing transformations only by the numbers given by the paper. Feel free to contact me if you know more accurate calibrations.

Customization

Right now you have to change the code in src/mulran2bag.cpp:

std::string gt_frame = "gt";
std::string base_frame = "base_link";

std::string ouster_topic = "os1_points";
std::string ouster_frame = "ouster";

std::string navtech_frame = "radar_polar";
std::string navtech_topic = "Navtech/Polar";

std::string xsens_frame = "imu";
std::string xsens_topic = "imu/data_raw";
std::string xsens_mag_topic = "imu/mag";

std::string gps_frame = "gps";
std::string gps_topic = "gps/fix";

std::string tf_static_topic = "tf_static";
std::string tf_topic = "tf";

// start ground truth tf at zero
// - disabling this leads to visualization issues
//   in RViz because of floating point issues
bool zero_gt_tf = true;

// enable parts of export
bool add_ouster = true;
bool add_radar = true;
bool add_imu = true;
bool add_gps = true;
bool add_dynamic_tf = true;
bool add_static_tf = true;

// hardcode imu covariances
double imu_orientation_cov[3] = {0.01, 0.02, 0.04};
double imu_ang_vel_cov[3]     = {0.002, 0.002, 0.002};
double imu_lin_acc_cov[3]     = {0.1, 0.1, 0.1};

All default values were taken from file_player_mulran.

Update 6 Sep, 23: Replaced all imu covariance defaults with new guesses based on MTi 300 datasheet. TODO: Try if guesses are valid

Issues

GPS

I do not know what model it is, nor where it is located. Data is published on gps/fix topic.

Xsens IMU

Because the file in the raw data is called xsens_imu.csv, I assume that it is a Xsens IMU. I do not know what model it is, nor where it is located. The data is published in the ìmu/data_raw section. As soon as magnetometer data is found, another topic will be created: ìmu/mag.

Warning: Covariances are hardcoded, with same default values as file_player_mulran.