LOCUS

LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings) is a Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time.

Build Instructions

See YouTube video

Install ROS

Install catkin tools

sudo apt-get install ros-melodic-catkin python-catkin-tools python3-catkin-tools # for the melodic distro - Ubuntu 18.04
sudo apt-get install ros-noetic-catkin python3-catkin-tools # for the noetc distro - Ubuntu 20.04

Install PCL_ROS

sudo apt-get install ros-melodic-pcl-ros # for the melodic distro - Ubuntu 18.04
sudo apt-get install ros-noetic-pcl-ros # for the noetc distro - Ubuntu 20.04

Install tf2_sensor_msgs, tf2_geometry_msgs and eigen_conversions

# for the melodic distro - Ubuntu 18.04
sudo apt install ros-melodic-tf2-sensor-msgs 
sudo apt install ros-melodic-tf2-geometry-msgs 
sudo apt install ros-melodic-eigen-conversions
# for the noetic distro - Ubuntu 20.04
sudo apt install ros-noetic-tf2-sensor-msgs 
sudo apt install ros-noetic-tf2-geometry-msgs 
sudo apt install ros-noetic-eigen-conversions

Install tmuxp (for our running scripts) tmuxp is a python wrapper for tmux allowing easy launching of multi-window, multi-tile tmux sessions.

sudo apt install tmux
sudo apt install tmuxp

Build this package in a catkin workspace, e.g.

mkdir -p catkin_ws/src
cd catkin_ws
catkin init
# For melodic install
catkin config -DCMAKE_BUILD_TYPE=Release --extend /opt/ros/melodic
# For noetic install
catkin config -DCMAKE_BUILD_TYPE=Release --extend /opt/ros/noetic
cd src
# With https
git clone https://github.com/johnd2010/LOCUS.git
git clone https://github.com/NeBula-Autonomy/common_nebula_slam.git
cd LOCUS
git checkout feature/jackal_test 
cd..
catkin build locus

Add the workspace sourcing to your bashrc (changing the filepath to match where your catkin workspace is)

echo "source ~/catkin_ws/devel/setup.bash" >> ~/.bashrc
source ~/.bashrc

Run Unit tests

rostest/gtest style unit tests are set up in numerous packages (not all) - run them all to check your install with:

cd ~/catkin_ws/src
catkin run_tests

Setup Clang formatting

For development, setup clang formatting for the repo

cd locus
./scripts/clang_setup/clang_setup.sh

This will install clang and put a pre-commit hook to maintain clang formatting. To force clang formatting on edited files, use:

git clang-format -f

Dockerfile

The docker provides an alternative, contained method for installation

Go to the docker folder, and

./cmd locus build
./cmd locus run
./cmd locus bash
cd ~/locus_ws/
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
catkin build
source devel/setup.bash

Running Instructions

This example is given for our open source datasets that come with ground truth: https://github.com/NeBula-Autonomy/nebula-odometry-dataset

There are many datsets there to test on, across a range of environments and with data from different robotic platforms.

Here, we give an example from dataset C, from a Husky Robot in the Statsop Power Plant, but the same steps apply for any other dataset we have released. The datset is large (45.8 GB), but to do a quick test, you can download just the first few lidar bags, and all non-lidar bags.

Note: the steps for running LOCUS live on the robot is no different from running on a dataset - the datasets are run and processed in real-time.

Download dataset

Download data from nebula-odometry-dataset

Download the rosbag data for dataset C (optionally just the first few lidar bags for a small test)
Download the Extras configuration files for dataset C.

Place the data in a common folder, e.g.

mkdir -p ~/data/C_Husky4_Urban_Alpha1  
mv ~/Downloads/C_Husky4_Urban_Alpha1_rosbag ~/data/C_Husky4_Urban_Alpha1/rosbag
mv ~/Downloads/C_Husky4_Urban_Alpha1_extra/* ~/data/C_Husky4_Urban_Alpha1/

i.e. have a root folder, with the bagfiles in a rosbag folder, and the other files in the root folder. The result should look something like:

/home/nebula/data/
└── C_Husky4_Urban_Alpha1
    ├── fiducial_calibration_husky4.yaml
    ├── h4_a1_gt_map.pcd
    ├── h4_a1_gt_map.ply
    ├── husky4_sensors.yaml
    ├── odometry.bag
    └── rosbag
        ├── imu.bag
        ├── odometry.bag
        ├── husky4_lidar_2020-02-21-15-04-16_0.bag 
        ├── husky4_lidar_2020-02-21-15-04-50_1.bag
        ├── ...

See here for an explanation of what each file is.

Edit the tmux script

Edit our tmux running script LOCUS/tmuxp_config/run_locus.yaml and modify the filepaths for your system. From the main branch, you should only need to modify the DATA_PATH

DATA_PATH - to point do your data folder, e.g. /home/nebula/data/C_Husky4_Urban_Alpha1

For other datasets, you will need to modify other parameters, such as:

ROBOT_NAME and ROBOT_TYPE to match the dataset, e.g. husky4 and husky
RUN_NUMBER to create a unique folder name for the output data to be stored

Other parameters are explaing in the yaml file.

Visualize tf tree

To run locus with tf tree run python script from locus/scripts

rosrun locus odom_to_map_tf.py <robot_ns>

i.e. for Nebula-costar-odometry datasets

rosrun locus odom_to_map_tf.py husky4

(it visualizes the path as well)

Run the test

Start a roscore in one terminal

roscore

Run the tumxp script in another terminal

tmuxp load {path_to_locus}/LOCUS/tmuxp_config/run_locus.yaml

The whole process should run something like:

Output data

At the end of the run, go to the third tmux window (ctrl + b, then n to cycle windows) and hit enter on the last pane (ctrl + b then arrow keys to navigate panes) to execute rosnode kill -a to stop all the bag recordings (will also kill RViz and locus).

To kill the tmux session, enter tmux kill-session.

The tmux script will store output odometry, map and stats in a new folder locus_$RUN_NUMBER in the root folder of the dataset. For example, the resulting folder should look like:

└── C_Husky4_Urban_Alpha1
    ├── fiducial_calibration_husky4.yaml
    ├── h4_a1_gt_map.pcd
    ├── h4_a1_gt_map.ply
    ├── husky4_sensors.yaml
    ├── locus_test_01
    ├── odometry.bag
    │   ├── delay.txt
    │   ├── lidar_callback_duration.bag
    │   ├── odometry.bag
    │   ├── rate.txt
    │   ├── scan_to_map_duration.bag
    │   ├── scan_to_scan_duration.bag
    │   └── stationary.bag
    └── rosbag
        ├── husky4_lidar_2020-02-21-15-04-16_0.bag
        ├── ...

Running on other NeBula datasets

See other tmuxp configs for each of our datasets in the LOCUS/tmux_config/run_nebula_odometry_dataset folder.

Note that the sensor configurations are not the same for every dataset (they are included in the extra folder), even if the robot name is the same. The tmuxp script handles copying the sensor config (e.g. husky4_sensors.yaml) into LOCUS. Having the wrong sensor config will lead to poor results.

The tmuxp script also copies the fiducial calibration file (e.g. fiducial_calibration_husky4.yaml), which gives the correct initial position to match the ground truth odometry. If this is not updated, the initial pose will be wrong. If it does not exist in your ~/.ros folder, the robot will start at the origin with zero rotation. This only matters if you want to compare to our ground truth.

Note: if you want to have locus end automatically after the dataset is finished, uncomment line 109 of the tmuxp config files.

Running LOCUS on your own datasets / Robot

There are four main parts to change in LOCUS to set up for your own robot

Sensor calibration (mainly lidar to IMU, lidar to base_link calibrations)
Body filter dimensions
Scan to map parameters
Launch parameters

In general, it is recommended to start with an example of a Spot or Husky, and modify it to suit your needs - i.e. just call your robot husky1 for the purposes of testing with LOCUS.

Sensor Calibration

You can see the sensor calibration files in LOCUS/sensor_description/config, e.g. husky4_sensors.yaml. There are multiple transfers there, but the main frames we care about for LOCUS are:

base_link - this is the frame assumed for all odometry inputs, and is the frame in which we compute odometry (lidar and IMU is transformed to this frame)
arch - for husky this is an interim frame from which the sensor poses are described
velodyne, velodyne_front and velodyne_rear - the frames for each of the velodynes
vn100 - the frame of the IMU

Other frames are defined in the URDF in LOCUS/sensor_description/urdf (e.g. arch to base_link).

Change these transforms for your robot to get the correct lidar to IMU transform, and those respective frame transforms to base_link. The same frame names are expected (e.g. {robot_name}/base_link)

Body filter dimensions

The body filter removes points from around the robot (e.g. self-occlusion). These parameters are found in LOCUS/locus/config: body_filter_params_husky.yaml and body_filter_params_spot.yaml. Modify one of these to create a box appropriate for your robot.

Scan to Map parameters

It is not essential to change these parameters, but you can see two different versions in LOCUS/point_cloud_localization/config for Spot and Husky. The main adjustment to consider is the number of ICP iterations.

Launch parameters

Go to the launch file in LOCUS/locus/launch/locus.launch. Modify the following:

ODOMETRY_TOPIC - to match your input odometry (optional)
b_integrate_interpolated_odom - choose true or false to match your needs. This will interpolate the prior input odom to compute the transforms and is recommended for low rate odom inputs (e.g. 20 Hz) - for inputs of 50 Hz and above it is recommended to use false.
Number of threads - for localization/num_threads, icp/num_threads, mapper/num_threads and the voxel filter num_threads - based on your computational capability (e.g. Spot uses 1 core, Husky uses 4)

Motion Distortion Correction

As of now, our motion distorition correction (MDC) is in our velodyne driver, hence is not internal to LOCUS at this stage. If your datasets has large rate rotations, this will result in a reduction in accuracy for data without separate MDC.

We are working on releasing a way to run our MDC on velodyne packets for those running on their own data.

Cite

If you use this code, or compare to it in your work, please cite the following:

@ARTICLE{reinke2022iros,
  author={Reinke, Andrzej and Palieri, Matteo and Morrell, Benjamin and Chang, Yun and Ebadi, Kamak and Carlone, Luca and Agha-Mohammadi, Ali-Akbar},
  journal={IEEE Robotics and Automation Letters}, 
  title={LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping}, 
  year={2022},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/LRA.2022.3181357}}

See examples of an earlier verison of LOCUS running here: https://www.youtube.com/watch?v=b2lBQ-q5rOE

@article{LOCUS,
  title={{LOCUS} - {A} {M}ulti-{S}ensor {L}idar-{C}entric {S}olution for {H}igh-{P}recision {O}dometry and 3{D} {M}apping in {R}eal-{T}ime},
  author={Palieri, Matteo and Morrell, Benjamin and Thakur, Abhishek and Ebadi, Kamak and Nash, Jeremy and Chatterjee, Arghya and Kanellakis, Christoforos and Carlone, Luca and Guaragnella, Cataldo and Agha-mohammadi, A.},
  journal={IEEE Robotics and Automation Letters},
  volume={6},
  number={2},
  pages={421--428},
  year={2020},
  publisher={IEEE}
}

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README.md

README.md

LOCUS

Build Instructions

Run Unit tests

Setup Clang formatting

Dockerfile

Running Instructions

Download dataset

Edit the tmux script

Visualize tf tree

Run the test

Output data

Running on other NeBula datasets

Running LOCUS on your own datasets / Robot

Sensor Calibration

Body filter dimensions

Scan to Map parameters

Launch parameters

Motion Distortion Correction

Cite

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README.md

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README.md

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LOCUS

Build Instructions

Run Unit tests

Setup Clang formatting

Dockerfile

Running Instructions

Download dataset

Edit the tmux script

Visualize tf tree

Run the test

Output data

Running on other NeBula datasets

Running LOCUS on your own datasets / Robot

Sensor Calibration

Body filter dimensions

Scan to Map parameters

Launch parameters

Motion Distortion Correction

Cite