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.

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.

See details.

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 was generated by using the cartographer_ros and filtered by using pcl_outlier_removal and pcl_voxel_grid utility.

mcl_3dl is available under BSD license.