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mapping: as11 | autonomous_mapping | furniture_classification | furniture_features | handle_detection | handle_detection2D | ias_projected_light | ias_table_srvs | kinect_cleanup | mapping_ias_msgs | next_best_view | pcl_cloud_algos | pcl_cloud_tools | pcl_ias_sample_consensus | pcl_to_octree | pcl_vtk_tools | pointcloud_registration | pr2_data_acquisition_tools | rgbd_registration | vosch

Package Summary

Documented

This package aligns two point clouds together by combining 2D RGB feature matching and a modified Iterative Closest Point (ICP) algorithm in one "Joint Optimization" step. It is not a SLAM system, it simply aligns two point clouds at a time.

mapping: as11 | autonomous_mapping | furniture_classification | furniture_features | handle_detection | handle_detection2D | ias_projected_light | ias_table_srvs | kinect_cleanup | mapping_ias_msgs | next_best_view | pcl_cloud_algos | pcl_cloud_tools | pcl_ias_sample_consensus | pcl_to_octree | pcl_vtk_tools | pointcloud_registration | pr2_data_acquisition_tools | rgbd_registration | vosch

Package Summary

Documented

This package aligns two point clouds together by combining 2D RGB feature matching and a modified Iterative Closest Point (ICP) algorithm in one "Joint Optimization" step. It is not a SLAM system, it simply aligns two point clouds at a time.

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Method

This algorithm consists of two main parts;

  • Extracting and matching 2D RGB features from the images
  • Performing a variant of ICP, or "Joint Optimization" incorporating both dense pointcloud information and 2D feature matches

The first step uses standard computer vision approaches to extract and match features. Various extraction and matching methods may be configured in the launch file. The feature points are then projected into 3D space using the associated point clouds. RANSAC is used to find a transformation that best fits the movement of 3D points between frames. The outliers are discarded and the 3D locations' of the inliers are used in the next step.

The next step is the variant of ICP. It combines both 3D visual features (sparse points) and pointcloud data (dense points) by using the visual features as distinct point correspondences. These correspondences are assumed to be correct and not recalculated on each iteration. The distance error function to minimize is a weighted combination of point to point distance of the sparse distinct points and point to plane distance of the dense points. The weighting can be changed by the variable alpha which is a parameter of this implementation.

A more detailed description of this approach was in the 3D mapping paper from Intel labs link. An evaluation of this approach can be found here.

Implementation

The feature extraction and matching has been implemented using opencv. The RANSAC step uses some functions from PCL but is mostly implemented in this package. The Joint Optimization was done by extending PCL's registration module.

Installation Instructions

git clone http://code.in.tum.de/git/mapping.git
cd mapping/rgbd_registration
make

The makefile will download some sample test data (~130MB download) from the internet. This can be disabled in the CMakeLists.txt

The test data is a sample from the RGB-D SLAM Dataset and Benchmark. There are 4 scenes with the presence or absences of texture and structure.

Usage

roslaunch rgbd_registration rgbd_registration.launch 

The package has just one executable and is best started with the launch file rgbd_registration.launch. The launch file handles file input and output, debugging and algorithm parameters. This launch file contains all the available parameters, their defaults and descriptions of what they do.

The results can be found in the results directory. The transformed pointcloud will be written here, along with other files as selected in the launch file. The "source" pointcloud is transformed to the "target". So to see the alignment one should view both the target and transformed pointclouds together:

cd results
pcd_viewer target_cloud.pcd transformed_cloud.pcd 

ROS Parameters

Please consult rgbd_registration.launch for a full list of parameters and descriptions

Wiki: rgbd_registration (last edited 2012-10-02 18:32:35 by RossKidson)