icp setIndices doesn't work

I first generate 1000 random points to form the source point cloud:
for (size_t i = 0; i < cloud_in->points.size (); ++i)
{
cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
}
and then form the target point cloud from the front 100 points of source point cloud , and add 0.7 to x
for (size_t i = 0; i < cloud_out->points.size (); ++i)
{
cloud_out->points[i].x = cloud_in->points[i].x + 0.7f;
cloud_out->points[i].y = cloud_in->points[i].y;
cloud_out->points[i].z = cloud_in->points[i].z;
}
I try two times for the indices:
boost::shared_ptr< std::vector > pr(new std::vector);
// first try
// for(int i=0; i<100; i+=1)
// pr->push_back(i);
// second try
for(int i=100; i<200; i+=1)
pr->push_back(i);
but the result transformation is the same, if the setIndices works, the result shouldn't be the same, because the points in source point cloud we use to registration is different.

Your Environment

• Operating System and version: ubuntu16.04
• Compiler:cmake
• PCL Version:1.7

Context

Expected Behavior

Current Behavior

Code to Reproduce

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);

    // Fill in the CloudIn data
    cloud_in->width    = 1000;
    cloud_in->height   = 1;
    cloud_in->is_dense = false;
    cloud_in->points.resize (cloud_in->width * cloud_in->height);
    for (size_t i = 0; i < cloud_in->points.size (); ++i)
    {
        cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
        cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
        cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
    }

    cloud_out->width    = 100;
    cloud_out->height   = 1;
    cloud_out->is_dense = false;
    cloud_out->points.resize (cloud_out->width * cloud_out->height);

    for (size_t i = 0; i < cloud_out->points.size (); ++i)
    {
        cloud_out->points[i].x = cloud_in->points[i].x + 0.7f;
        cloud_out->points[i].y = cloud_in->points[i].y;
        cloud_out->points[i].z = cloud_in->points[i].z;
    }
    std::cout << cloud_out->points.size() << std::endl;


    boost::shared_ptr< std::vector<int> > pr(new std::vector<int>);
   // first try
   //   for(int i=0; i<100; i+=1)
    //    pr->push_back(i);
 // second try
    for(int i=100; i<200; i+=1)
        pr->push_back(i);

    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
    icp.setMaxCorrespondenceDistance(0.5);
    icp.setTransformationEpsilon(1e-12);
    icp.setEuclideanFitnessEpsilon(0.0001);
    icp.setMaximumIterations (1000000);
    icp.setInputSource(cloud_in);
    icp.setInputTarget(cloud_out);

    //icp.setIndices(pr);
    pcl::PointCloud<pcl::PointXYZ> Final;
    icp.align(Final);

    std::cout << "has converged:" << icp.hasConverged() << " score: " <<
              icp.getFitnessScore() << std::endl;

    std::cout << icp.getFinalTransformation() << std::endl;

    pcl::transformPointCloud(*cloud_out,*cloud_out,icp.getFinalTransformation().inverse());

Possible Solution

Start by running the supplied C++ reproduction with PCL 1.7 and compare results with icp.setIndices(pr) enabled for the two index ranges. Inspect the IterativeClosestPoint setIndices entry point and related index handling. Done means the behavior is explained and, if incorrect, a focused fix or regression test demonstrates that changing source indices affects registration.