Create an octree instance

This octree keeps a vector of point indices within its leaf nodes.

The resolution parameter describes the length of the smallest voxels at lowest octree level.

float resolution = 128.0f;

pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);

octree.setInputCloud (cloud);
octree.addPointsFromInputCloud ();

Perform search operations

KNN Search

// K nearest neighbor search

int K = 10;

std::vector<int> pointIdxNKNSearch;
std::vector<float> pointNKNSquaredDistance;

std::cout << "K nearest neighbor search at (" << searchPoint.x 
          << " " << searchPoint.y 
          << " " << searchPoint.z
          << ") with K=" << K << std::endl;

if (octree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
{
  for (std::size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
    std::cout << "    "  <<   (*cloud)[ pointIdxNKNSearch[i] ].x 
              << " " << (*cloud)[ pointIdxNKNSearch[i] ].y 
              << " " << (*cloud)[ pointIdxNKNSearch[i] ].z 
              << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
}

Neighbors within Radius Search

Its search results are written to two separate vectors describing point indices and squares search point distances.

std::vector<int> pointIdxRadiusSearch;
std::vector<float> pointRadiusSquaredDistance;

float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

std::cout << "Neighbors within radius search at (" << searchPoint.x 
    << " " << searchPoint.y 
    << " " << searchPoint.z
    << ") with radius=" << radius << std::endl;

if (octree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
{
  for (std::size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
    std::cout << "    "  <<   (*cloud)[ pointIdxRadiusSearch[i] ].x 
              << " " << (*cloud)[ pointIdxRadiusSearch[i] ].y 
              << " " << (*cloud)[ pointIdxRadiusSearch[i] ].z 
              << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
}

Example output

Neighbors within voxel search at (974.82 188.793 138.779)
    903.656 82.8158 162.392
    1007.34 191.035 61.7727
    896.88 155.711 58.1942
K nearest neighbor search at (974.82 188.793 138.779)with K=10
    903.656 82.8158 162.392 (squared distance: 16853.1)
    903.18 247.058 54.3528 (squared distance: 15655)
    861.595 149.96 135.199 (squared distance: 14340.7)
    896.88 155.711 58.1942 (squared distance: 13663)
    995.889 116.224 219.077 (squared distance: 12157.9)
    885.852 238.41 160.966 (squared distance: 10869.5)
    900.807 220.317 77.1432 (squared distance: 10270.7)
    1002.46 117.236 184.594 (squared distance: 7983.59)
    1007.34 191.035 61.7727 (squared distance: 6992.54)
    930.13 223.335 174.763 (squared distance: 4485.15)
Neighbors within radius search at (974.82 188.793 138.779)with radius=109.783
    1007.34 191.035 61.7727 (squared distance: 6992.54)
    900.807 220.317 77.1432 (squared distance: 10270.7)
    885.852 238.41 160.966 (squared distance: 10869.5)
    1002.46 117.236 184.594 (squared distance: 7983.59)
    930.13 223.335 174.763 (squared distance: 4485.15)

Different octree PCL components

• OctreePointCloudPointVector (equal to OctreePointCloud): This octree can hold a list of point indices at each leaf node.
• OctreePointCloudSinglePoint: This octree class hold only a single point indices at each leaf node. Only the most recent point index that is assigned to the leaf node is stored.
• OctreePointCloudOccupancy: This octree does not store any point information at its leaf nodes. It can be used for spatial occupancy checks.
• OctreePointCloudDensity: This octree counts the amount of points within each leaf node voxel. It allows for spatial density queries.