• A robust radar odometry method in urban environments

• Please refer our paper and YouTube for detailed explanations and experimental results!

• Validated on MulRan dataset.

• CMake version > 3.13

• gcc/g++ > 9.0

• In Ubuntu 18.04, we use

  • Eigen 3.3
  • Boost 1.5.8
  • OpenCV 3.3 (or 3.4)

• In Ubuntu 20.04, we use

  • Eigen 3.3
  • Boost 1.71
  • OpenCV 4.2

Just follow the below command:

$ mkdir -p \~/catkin_ws/src/ && cd \~/catkin_ws/src
$ git clone https://github.com/url-kaist/outlier-robust-radar-odometry.git
$ cd ..
$ catkin build orora

TMI: Following the philosophy of target-oriented CMake, PMC is automatically installed when you run catkin build orora

1. Set file tree of the MulRan dataset as follows:

${MULRAN_DIR}
_____/KAIST03 
     |___global_pose.csv
     |___/gt (Synchonized GT poses are saved)
         |___... 
     |___polar_oxford_form
         |___1567410201812840928.png
         |___1567410202062436262.png
         |___1567410202312110509.png
         |___...    
_____${OTHER SEQ}
     |...
_____...
   

2. Generate synchronized ground truth poses to the radar data as follows:

$ rosrun orora mulran_generate_gt ${MULRAN_DIR} ${SEQ1} ${SEQ2}...
// e.g.
$ rosrun orora mulran_generate_gt /media/hyungtaelim/UX960NVMe/mulran KAIST03

3. Then, set right seq_dir in launch/run_orora.launch & run the below command

roslaunch orora run_orora.launch seq_dir:=${DATA_DIR}

For instance, it can be run as follows:

roslaunch orora run_orora.launch seq_dir:="/media/shapelim/UX980/UX960NVMe/mulran-radar/KAIST03"

Note, polar_oxford_form folder should be placed in the ${DATA_DIR} directory.

4. Run script/evaluate_odometry.py as follows:

// E.g.
$ python evaluate_odometry.py -f /media/hyungtaelim/UX960NVMe/mulran/KAIST03/outputs/mulran_ORORA_cen2018_0.6_0.75_0.1_0.15708eval_odom.txt

Many thanks to Keenan Burnett to provide outstanding radar odometry codes!

Please refer to Yeti-Radar-Odometry for more information

If you have any questions, please do not hesitate to contact us

• Hyungtae Lim ✉️ shapelim at kaist.ac.kr