Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/NeBula-Autonomy/LOCUS

Robust Lidar Odometry System
https://github.com/NeBula-Autonomy/LOCUS

lidar-odometry ros slam

Last synced: 2 months ago
JSON representation

Robust Lidar Odometry System

Lists

README 

        
# 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.



![alt text](media/readme.png)



# Build Instructions



See [YouTube video](https://youtu.be/f_S4_6deQuI)



Install [ROS](http://wiki.ros.org/ROS/Installation)



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. 



```bash

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 ssh

git clone [email protected]:NeBula-Autonomy/LOCUS.git

git clone [email protected]:NeBula-Autonomy/common_nebula_slam.git

# With https

git clone https://github.com/NeBula-Autonomy/LOCUS.git

git clone https://github.com/NeBula-Autonomy/common_nebula_slam.git

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](https://github.com/NeBula-Autonomy/nebula-odometry-dataset/blob/main/pages/dataset.md)

1. Download the rosbag data for [dataset C](https://nextcloud.robotics.caltech.edu/index.php/s/6QNKyLN85WqN9Rd) (optionally just the first few lidar bags for a small test)

2. Download the [Extras configuration files](https://nextcloud.robotics.caltech.edu/index.php/s/TyoFS93rRo5rgXa) 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](https://github.com/NeBula-Autonomy/nebula-odometry-dataset/blob/main/pages/dataset.md) 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 

```



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:



![video of locus runnig](media/locus_running.gif)



## 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

1. Sensor calibration (mainly lidar to IMU, lidar to base_link calibrations)

2. Body filter dimensions 

3. Scan to map parameters

4. 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:

1. `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)

2. `arch` - for husky this is an interim frame from which the sensor poses are described

3. `velodyne`, `velodyne_front` and `velodyne_rear` - the frames for each of the velodynes

4. `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:

1. `ODOMETRY_TOPIC` - to match your input odometry (optional)

2. `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`.

3. 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}

}

```