231 lines
6.2 KiB
Text
231 lines
6.2 KiB
Text
/*
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* kdManaged implementation
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*
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* Copyright (C) Andreas Nuechter, Kai Lingemann, Thomas Escher
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*
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* Released under the GPL version 3.
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*
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*/
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/** @file
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* @brief An optimized k-d tree implementation
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* @author Andreas Nuechter. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Kai Lingemann. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Thomas Escher. Institute of Computer Science, University of Osnabrueck, Germany.
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*/
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#ifdef _MSC_VER
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#define _USE_MATH_DEFINES
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#endif
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#include "slam6d/kdManaged.h"
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#include "slam6d/scan.h"
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#include "slam6d/globals.icc"
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#include <iostream>
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using std::cout;
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using std::cerr;
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using std::endl;
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#include <algorithm>
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using std::swap;
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#include <cmath>
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#include <cstring>
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// KDtree class static variables
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KDParams KDtreeManagedBase::params[MAX_OPENMP_NUM_THREADS];
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/**
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* Constructor
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*
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* Create a KD tree from the points pointed to by the array pts
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*
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* @param pts 3D array of points
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* @param n number of points
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*/
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KDtreeManagedBase::KDtreeManagedBase(const DataXYZ& pts, unsigned int* indices, unsigned int n)
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{
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// Find bbox
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double xmin = pts[indices[0]][0], xmax = pts[indices[0]][0];
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double ymin = pts[indices[0]][1], ymax = pts[indices[0]][1];
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double zmin = pts[indices[0]][2], zmax = pts[indices[0]][2];
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for(unsigned int i = 1; i < n; i++) {
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xmin = min(xmin, pts[indices[i]][0]);
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xmax = max(xmax, pts[indices[i]][0]);
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ymin = min(ymin, pts[indices[i]][1]);
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ymax = max(ymax, pts[indices[i]][1]);
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zmin = min(zmin, pts[indices[i]][2]);
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zmax = max(zmax, pts[indices[i]][2]);
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}
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// Leaf nodes
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if ((n > 0) && (n <= 10)) {
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npts = n;
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leaf.p = new unsigned int[n];
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// fill leaf index array with indices
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for(unsigned int i = 0; i < n; ++i) {
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leaf.p[i] = indices[i];
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}
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return;
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}
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// Else, interior nodes
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npts = 0;
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node.center[0] = 0.5 * (xmin+xmax);
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node.center[1] = 0.5 * (ymin+ymax);
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node.center[2] = 0.5 * (zmin+zmax);
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node.dx = 0.5 * (xmax-xmin);
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node.dy = 0.5 * (ymax-ymin);
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node.dz = 0.5 * (zmax-zmin);
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node.r2 = sqr(node.dx) + sqr(node.dy) + sqr(node.dz);
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// Find longest axis
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if (node.dx > node.dy) {
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if (node.dx > node.dz) {
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node.splitaxis = 0;
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} else {
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node.splitaxis = 2;
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}
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} else {
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if (node.dy > node.dz) {
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node.splitaxis = 1;
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} else {
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node.splitaxis = 2;
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}
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}
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// Partition
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double splitval = node.center[node.splitaxis];
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if ( fabs(max(max(node.dx,node.dy),node.dz)) < 0.01 ) {
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npts = n;
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leaf.p = new unsigned int[n];
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// fill leaf index array with indices
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for(unsigned int i = 0; i < n; ++i) {
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leaf.p[i] = indices[i];
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}
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return;
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}
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unsigned int* left = indices, * right = indices + n - 1;
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while(true) {
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while(pts[*left][node.splitaxis] < splitval)
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left++;
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while(pts[*right][node.splitaxis] >= splitval)
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right--;
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if(right < left)
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break;
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swap(*left, *right);
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}
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// Build subtrees
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int i;
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#ifdef WITH_OPENMP_KD // does anybody know the reason why this is slower ?? --Andreas
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omp_set_num_threads(OPENMP_NUM_THREADS);
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#pragma omp parallel for schedule(dynamic)
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#endif
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for (i = 0; i < 2; i++) {
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if (i == 0) node.child1 = new KDtreeManagedBase(pts, indices, left - indices);
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if (i == 1) node.child2 = new KDtreeManagedBase(pts, left, n - (left - indices));
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}
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}
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KDtreeManagedBase::~KDtreeManagedBase()
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{
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if (!npts) {
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#ifdef WITH_OPENMP_KD
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omp_set_num_threads(OPENMP_NUM_THREADS);
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#pragma omp parallel for schedule(dynamic)
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#endif
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for (int i = 0; i < 2; i++) {
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if (i == 0 && node.child1) delete node.child1;
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if (i == 1 && node.child2) delete node.child2;
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}
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} else {
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if (leaf.p) delete [] leaf.p;
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}
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}
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/**
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* Wrapped function
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*/
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void KDtreeManagedBase::_FindClosest(const DataXYZ& pts, int threadNum) const
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{
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// Leaf nodes
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if (npts) {
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for (int i = 0; i < npts; i++) {
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double myd2 = Dist2(params[threadNum].p, pts[leaf.p[i]]);
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if (myd2 < params[threadNum].closest_d2) {
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params[threadNum].closest_d2 = myd2;
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params[threadNum].closest = pts[leaf.p[i]];
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}
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}
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return;
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}
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// Quick check of whether to abort
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double approx_dist_bbox = max(max(fabs(params[threadNum].p[0]-node.center[0])-node.dx,
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fabs(params[threadNum].p[1]-node.center[1])-node.dy),
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fabs(params[threadNum].p[2]-node.center[2])-node.dz);
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if (approx_dist_bbox >= 0 && sqr(approx_dist_bbox) >= params[threadNum].closest_d2)
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return;
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// Recursive case
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double myd = node.center[node.splitaxis] - params[threadNum].p[node.splitaxis];
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if (myd >= 0.0) {
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node.child1->_FindClosest(pts, threadNum);
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if (sqr(myd) < params[threadNum].closest_d2) {
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node.child2->_FindClosest(pts, threadNum);
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}
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} else {
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node.child2->_FindClosest(pts, threadNum);
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if (sqr(myd) < params[threadNum].closest_d2) {
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node.child1->_FindClosest(pts, threadNum);
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}
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}
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}
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KDtreeManaged::KDtreeManaged(Scan* scan) :
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KDtreeManagedBase(scan->get("xyz reduced original"), prepareTempIndices(scan->size<DataXYZ>("xyz reduced original")), scan->size<DataXYZ>("xyz reduced original")),
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m_scan(scan), m_data(0), m_count_locking(0)
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{
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// allocate in prepareTempIndices, deleted here
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delete[] m_temp_indices;
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}
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unsigned int* KDtreeManaged::prepareTempIndices(unsigned int n)
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{
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m_temp_indices = new unsigned int[n];
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for(unsigned int i = 0; i < n; ++i)
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m_temp_indices[i] = i;
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return m_temp_indices;
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}
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double* KDtreeManaged::FindClosest(double *_p, double maxdist2, int threadNum) const
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{
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params[threadNum].closest = 0;
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params[threadNum].closest_d2 = maxdist2;
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params[threadNum].p = _p;
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_FindClosest(*m_data, threadNum);
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return params[threadNum].closest;
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}
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void KDtreeManaged::lock()
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{
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boost::lock_guard<boost::mutex> lock(m_mutex_locking);
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++m_count_locking;
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if(m_data == 0) {
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// aquire an array lock from the scan and hold it while the tree is in use
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m_data = new DataXYZ(m_scan->get("xyz reduced original"));
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}
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}
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void KDtreeManaged::unlock()
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{
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boost::lock_guard<boost::mutex> lock(m_mutex_locking);
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--m_count_locking;
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if(m_count_locking == 0 && m_data != 0) {
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delete m_data;
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m_data = 0;
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}
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}
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