mcl_3dl repository

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2020-09-18
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
mcl_3dl 0.3.0

README

mcl_3dl

Build Status codecov License

Package summary

mcl_3dl is a ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.

The node receives the reference pointcloud as an environment map and localizes 6-DOF (x, y, z, yaw, pitch, roll) pose of measured pointclouds assisted by a motion prediction using odometry.

Currently, the supported motion model is differential-wheeled-robot. The node provides classic MCL; currently, it doesn't implement adaptive feature like KDL-sampling and etc.

Algorithms

A fundamental algorithm of mcl_3dl node is Monte Carlo localization (MCL), aka particle filter localization. MCL represents a probabilistic distribution of estimated pose as density and weight of particles and estimates the pose from the distribution.

Node I/O

mcl_3dl I/O diagram

Install

from source

Note: mcl_3dl_msgs package is required to build mcl_3dl package.

# clone
cd /path/to/your/catkin_ws/src
git clone https://github.com/at-wat/mcl_3dl.git
git clone https://github.com/at-wat/mcl_3dl_msgs.git

# build
cd /path/to/your/catkin_ws
rosdep install --from-paths src --ignore-src -y  # Install dependencies
catkin_make -DCMAKE_BUILD_TYPE=Release  # Release build is recommended

from apt repository (for ROS Indigo/Kinetic/Lunar on Ubuntu)

sudo apt-get install ros-${ROS_DISTRO}-mcl-3dl

Running the demo

The example bag file of 2+4-DOF tracked vehicle with two Hokuyo YVT-X002 3-D LIDAR is available online. Pre-processed (filtered) 3-D pointcloud, IMU pose, odometry, and map data are packed in the bag.

# Download the example bag (230M)
wget -P ~/Downloads https://openspur.org/~atsushi.w/dataset/mcl_3dl/short_test3.bag

# Running the demo
roslaunch mcl_3dl test.launch use_pointcloud_map:=false use_cad_map:=false \
  use_bag_file:=true bag_file:=${HOME}/Downloads/short_test3.bag

The map data in the bag was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utilities.

Rviz image of the demo

MarkerArray shows several mcl_3dl internal information. - Purple spheres: sampled points used in the likelihood-model calculation - Red lines: casted rays in the beam-model calculation - Red boxes: detected collisions in raycasting

To try global localization, call /global_localization by the following command.

rosservice call /global_localization

Demos without odometry and without IMU are also available.

Contributing

mcl_3dl package is developed under GitHub flow. Feel free to open new Issue and/or Pull Request.

The code in this repository is following ROS C++ Style Guide. A configuration file for clang-format is available at https://github.com/seqsense/ros_style/.

License

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2020-09-18
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
mcl_3dl 0.3.0

README

mcl_3dl

Build Status codecov License

Package summary

mcl_3dl is a ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.

The node receives the reference pointcloud as an environment map and localizes 6-DOF (x, y, z, yaw, pitch, roll) pose of measured pointclouds assisted by a motion prediction using odometry.

Currently, the supported motion model is differential-wheeled-robot. The node provides classic MCL; currently, it doesn't implement adaptive feature like KDL-sampling and etc.

Algorithms

A fundamental algorithm of mcl_3dl node is Monte Carlo localization (MCL), aka particle filter localization. MCL represents a probabilistic distribution of estimated pose as density and weight of particles and estimates the pose from the distribution.

Node I/O

mcl_3dl I/O diagram

Install

from source

Note: mcl_3dl_msgs package is required to build mcl_3dl package.

# clone
cd /path/to/your/catkin_ws/src
git clone https://github.com/at-wat/mcl_3dl.git
git clone https://github.com/at-wat/mcl_3dl_msgs.git

# build
cd /path/to/your/catkin_ws
rosdep install --from-paths src --ignore-src -y  # Install dependencies
catkin_make -DCMAKE_BUILD_TYPE=Release  # Release build is recommended

from apt repository (for ROS Indigo/Kinetic/Lunar on Ubuntu)

sudo apt-get install ros-${ROS_DISTRO}-mcl-3dl

Running the demo

The example bag file of 2+4-DOF tracked vehicle with two Hokuyo YVT-X002 3-D LIDAR is available online. Pre-processed (filtered) 3-D pointcloud, IMU pose, odometry, and map data are packed in the bag.

# Download the example bag (230M)
wget -P ~/Downloads https://openspur.org/~atsushi.w/dataset/mcl_3dl/short_test3.bag

# Running the demo
roslaunch mcl_3dl test.launch use_pointcloud_map:=false use_cad_map:=false \
  use_bag_file:=true bag_file:=${HOME}/Downloads/short_test3.bag

The map data in the bag was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utilities.

Rviz image of the demo

MarkerArray shows several mcl_3dl internal information. - Purple spheres: sampled points used in the likelihood-model calculation - Red lines: casted rays in the beam-model calculation - Red boxes: detected collisions in raycasting

To try global localization, call /global_localization by the following command.

rosservice call /global_localization

Demos without odometry and without IMU are also available.

Contributing

mcl_3dl package is developed under GitHub flow. Feel free to open new Issue and/or Pull Request.

The code in this repository is following ROS C++ Style Guide. A configuration file for clang-format is available at https://github.com/seqsense/ros_style/.

License

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2020-09-18
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
mcl_3dl 0.3.0

README

mcl_3dl

Build Status codecov License

Package summary

mcl_3dl is a ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.

The node receives the reference pointcloud as an environment map and localizes 6-DOF (x, y, z, yaw, pitch, roll) pose of measured pointclouds assisted by a motion prediction using odometry.

Currently, the supported motion model is differential-wheeled-robot. The node provides classic MCL; currently, it doesn't implement adaptive feature like KDL-sampling and etc.

Algorithms

A fundamental algorithm of mcl_3dl node is Monte Carlo localization (MCL), aka particle filter localization. MCL represents a probabilistic distribution of estimated pose as density and weight of particles and estimates the pose from the distribution.

Node I/O

mcl_3dl I/O diagram

Install

from source

Note: mcl_3dl_msgs package is required to build mcl_3dl package.

# clone
cd /path/to/your/catkin_ws/src
git clone https://github.com/at-wat/mcl_3dl.git
git clone https://github.com/at-wat/mcl_3dl_msgs.git

# build
cd /path/to/your/catkin_ws
rosdep install --from-paths src --ignore-src -y  # Install dependencies
catkin_make -DCMAKE_BUILD_TYPE=Release  # Release build is recommended

from apt repository (for ROS Indigo/Kinetic/Lunar on Ubuntu)

sudo apt-get install ros-${ROS_DISTRO}-mcl-3dl

Running the demo

The example bag file of 2+4-DOF tracked vehicle with two Hokuyo YVT-X002 3-D LIDAR is available online. Pre-processed (filtered) 3-D pointcloud, IMU pose, odometry, and map data are packed in the bag.

# Download the example bag (230M)
wget -P ~/Downloads https://openspur.org/~atsushi.w/dataset/mcl_3dl/short_test3.bag

# Running the demo
roslaunch mcl_3dl test.launch use_pointcloud_map:=false use_cad_map:=false \
  use_bag_file:=true bag_file:=${HOME}/Downloads/short_test3.bag

The map data in the bag was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utilities.

Rviz image of the demo

MarkerArray shows several mcl_3dl internal information. - Purple spheres: sampled points used in the likelihood-model calculation - Red lines: casted rays in the beam-model calculation - Red boxes: detected collisions in raycasting

To try global localization, call /global_localization by the following command.

rosservice call /global_localization

Demos without odometry and without IMU are also available.

Contributing

mcl_3dl package is developed under GitHub flow. Feel free to open new Issue and/or Pull Request.

The code in this repository is following ROS C++ Style Guide. A configuration file for clang-format is available at https://github.com/seqsense/ros_style/.

License

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2020-09-18
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
mcl_3dl 0.3.0

README

mcl_3dl

Build Status codecov License

Package summary

mcl_3dl is a ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.

The node receives the reference pointcloud as an environment map and localizes 6-DOF (x, y, z, yaw, pitch, roll) pose of measured pointclouds assisted by a motion prediction using odometry.

Currently, the supported motion model is differential-wheeled-robot. The node provides classic MCL; currently, it doesn't implement adaptive feature like KDL-sampling and etc.

Algorithms

A fundamental algorithm of mcl_3dl node is Monte Carlo localization (MCL), aka particle filter localization. MCL represents a probabilistic distribution of estimated pose as density and weight of particles and estimates the pose from the distribution.

Node I/O

mcl_3dl I/O diagram

Install

from source

Note: mcl_3dl_msgs package is required to build mcl_3dl package.

# clone
cd /path/to/your/catkin_ws/src
git clone https://github.com/at-wat/mcl_3dl.git
git clone https://github.com/at-wat/mcl_3dl_msgs.git

# build
cd /path/to/your/catkin_ws
rosdep install --from-paths src --ignore-src -y  # Install dependencies
catkin_make -DCMAKE_BUILD_TYPE=Release  # Release build is recommended

from apt repository (for ROS Indigo/Kinetic/Lunar on Ubuntu)

sudo apt-get install ros-${ROS_DISTRO}-mcl-3dl

Running the demo

The example bag file of 2+4-DOF tracked vehicle with two Hokuyo YVT-X002 3-D LIDAR is available online. Pre-processed (filtered) 3-D pointcloud, IMU pose, odometry, and map data are packed in the bag.

# Download the example bag (230M)
wget -P ~/Downloads https://openspur.org/~atsushi.w/dataset/mcl_3dl/short_test3.bag

# Running the demo
roslaunch mcl_3dl test.launch use_pointcloud_map:=false use_cad_map:=false \
  use_bag_file:=true bag_file:=${HOME}/Downloads/short_test3.bag

The map data in the bag was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utilities.

Rviz image of the demo

MarkerArray shows several mcl_3dl internal information. - Purple spheres: sampled points used in the likelihood-model calculation - Red lines: casted rays in the beam-model calculation - Red boxes: detected collisions in raycasting

To try global localization, call /global_localization by the following command.

rosservice call /global_localization

Demos without odometry and without IMU are also available.

Contributing

mcl_3dl package is developed under GitHub flow. Feel free to open new Issue and/or Pull Request.

The code in this repository is following ROS C++ Style Guide. A configuration file for clang-format is available at https://github.com/seqsense/ros_style/.

License

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version indigo-devel
Last Updated 2019-05-21
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
mcl_3dl 0.1.4

README

mcl_3dl

Build Status codecov License

Package summary

mcl_3dl is a ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.

The node receives the reference pointcloud as an environment map and localizes 6-DOF (x, y, z, yaw, pitch, roll) pose of measured pointclouds assisted by a motion prediction using odometry.

Currently, the supported motion model is differential-wheeled-robot. The node provides classic MCL; currently, it doesn't implement adaptive feature like KDL-sampling and etc.

Algorithms

A fundamental algorithm of mcl_3dl node is Monte Carlo localization (MCL), aka particle filter localization. MCL represents a probabilistic distribution of estimated pose as density and weight of particles and estimates the pose from the distribution.

Node I/O

mcl_3dl I/O diagram

Install

from source

Note: mcl_3dl_msgs package is required to build mcl_3dl package.

# clone
cd /path/to/your/catkin_ws/src
git clone https://github.com/at-wat/mcl_3dl.git
git clone https://github.com/at-wat/mcl_3dl_msgs.git

# build
cd /path/to/your/catkin_ws
rosdep install --from-paths src --ignore-src -y  # Install dependencies
catkin_make -DCMAKE_BUILD_TYPE=Release  # Release build is recommended

from apt repository (for ROS Indigo/Kinetic/Lunar on Ubuntu)

sudo apt-get install ros-${ROS_DISTRO}-mcl-3dl

Running the demo

The example bag file of 2+4-DOF tracked vehicle with two Hokuyo YVT-X002 3-D LIDAR is available online. Pre-processed (filtered) 3-D pointcloud, IMU pose, odometry, and map data are packed in the bag.

# Download the example bag (230M)
wget -P ~/Downloads https://openspur.org/~atsushi.w/dataset/mcl_3dl/short_test.bag

# Running the demo
roslaunch mcl_3dl test.launch use_pointcloud_map:=false use_cad_map:=false \
  use_bag_file:=true bag_file:=${HOME}/Downloads/short_test.bag

The map data in the bag was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utilities.

Rviz image of the demo

MarkerArray shows several mcl_3dl internal information. - Purple spheres: sampled points used in the likelihood-model calculation - Red lines: casted rays in the beam-model calculation - Red boxes: detected collisions in raycasting

A demo without odometry is also available.

Contributing

mcl_3dl package is developed under GitHub flow. Feel free to open new Issue and/or Pull Request.

The code in this repository is following ROS C++ Style Guide. A configuration file for clang-format is available at https://github.com/seqsense/ros_style/.

License

CONTRIBUTING

No CONTRIBUTING.md found.