mcl_3dl repository

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2018-12-04
Dev Status DEVELOPED
Released RELEASED

Packages

Name Version
mcl_3dl 0.1.3

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

mcl_3dl is available under BSD license.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2018-12-04
Dev Status DEVELOPED
Released RELEASED

Packages

Name Version
mcl_3dl 0.1.3

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

mcl_3dl is available under BSD license.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2018-12-04
Dev Status DEVELOPED
Released RELEASED

Packages

Name Version
mcl_3dl 0.1.3

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

mcl_3dl is available under BSD license.

Repository Summary

Checkout URI https://github.com/at-wat/mcl_3dl.git
VCS Type git
VCS Version master
Last Updated 2018-12-04
Dev Status DEVELOPED
Released RELEASED

Packages

Name Version
mcl_3dl 0.1.3

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

mcl_3dl is available under BSD license.