double score(IGMap map, DoubleOrientedPoint p, double[] readings) {
double s = 0;
int angleIndex = initialBeamsSkip;
DoubleOrientedPoint lp = new DoubleOrientedPoint(p.x, p.y, p.theta);
lp.x += Math.cos(p.theta) * laserPose.x - Math.sin(p.theta) * laserPose.y;
lp.y += Math.sin(p.theta) * laserPose.x + Math.cos(p.theta) * laserPose.y;
lp.theta += laserPose.theta;
int skip = 0;
double freeDelta = map.getDelta() * freeCellRatio;
for (int rIndex = initialBeamsSkip; rIndex < readings.length; rIndex++, angleIndex++) {
skip++;
skip = skip > likelihoodSkip ? 0 : skip;
if (skip != 0 || readings[rIndex] > usableRange || readings[rIndex] == 0.0) continue;
DoublePoint phit = new DoublePoint(lp.x, lp.y);
phit.x += readings[rIndex] * Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex]));
phit.y += readings[rIndex] * Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex]));
IntPoint iphit = map.world2map(phit);
DoublePoint pfree = new DoublePoint(lp.x, lp.y);
pfree.x +=
(readings[rIndex] - map.getDelta() * freeDelta)
* Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex]));
pfree.y +=
(readings[rIndex] - map.getDelta() * freeDelta)
* Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex]));
pfree.x = pfree.x - phit.x;
pfree.y = pfree.y - phit.y;
IntPoint ipfree = map.world2map(pfree);
boolean found = false;
DoublePoint bestMu = new DoublePoint(0., 0.);
for (int xx = -kernelSize; xx <= kernelSize; xx++) {
for (int yy = -kernelSize; yy <= kernelSize; yy++) {
IntPoint pr = new IntPoint(iphit.x + xx, iphit.y + yy);
IntPoint pf = new IntPoint(pr.x + ipfree.x, pr.y + ipfree.y);
// int ss = map.getStorage().cellState(pr);
// if ((ss) > 0) {
PointAccumulator cell = (PointAccumulator) map.cell(pr, true);
PointAccumulator fcell = (PointAccumulator) map.cell(pf, true);
if (cell != null && fcell != null) {
if (cell.doubleValue() > fullnessThreshold && fcell.doubleValue() < fullnessThreshold) {
DoublePoint mu = DoublePoint.minus(phit, cell.mean());
if (!found) {
bestMu = mu;
found = true;
} else {
bestMu = DoublePoint.mulD(mu, mu) < DoublePoint.mulD(bestMu, bestMu) ? mu : bestMu;
}
}
}
// }
}
}
if (found) {
s += Math.exp(-1. / (gaussianSigma * DoublePoint.mulD(bestMu, bestMu)));
}
}
return s;
}
public double icpOptimize(
DoubleOrientedPoint pnew, IGMap map, DoubleOrientedPoint init, double[] readings) {
double currentScore;
double sc = score(map, init, readings);
DoubleOrientedPoint start = init;
pnew.x = init.x;
pnew.y = init.y;
pnew.theta = init.theta;
int iterations = 0;
do {
currentScore = sc;
sc = icpStep(pnew, map, start, readings);
start = pnew;
iterations++;
} while (sc > currentScore);
return currentScore;
}
public double icpStep(
DoubleOrientedPoint pret, IGMap map, DoubleOrientedPoint p, double[] readings) {
int angleIndex = initialBeamsSkip;
DoubleOrientedPoint lp = new DoubleOrientedPoint(p.x, p.y, p.theta);
lp.x += Math.cos(p.theta) * laserPose.x - Math.sin(p.theta) * laserPose.y;
lp.y += Math.sin(p.theta) * laserPose.x + Math.cos(p.theta) * laserPose.y;
lp.theta += laserPose.theta;
int skip = 0;
double freeDelta = map.getDelta() * freeCellRatio;
List<DoublePointPair> pairs = new ArrayList<DoublePointPair>();
for (int rIndex = initialBeamsSkip; rIndex < readings.length; rIndex++, angleIndex++) {
skip++;
skip = skip > likelihoodSkip ? 0 : skip;
if (readings[rIndex] > usableRange || readings[rIndex] == 0.0) continue;
if (skip != 0) continue;
DoublePoint phit = new DoublePoint(lp.x, lp.y);
phit.x += readings[rIndex] * Math.cos(lp.theta + laserAngles[angleIndex]);
phit.y += readings[rIndex] * Math.sin(lp.theta + laserAngles[angleIndex]);
IntPoint iphit = map.world2map(phit);
DoublePoint pfree = new DoublePoint(lp.x, lp.y);
pfree.x +=
(readings[rIndex] - map.getDelta() * freeDelta)
* Math.cos(lp.theta + laserAngles[angleIndex]);
pfree.y +=
(readings[rIndex] - map.getDelta() * freeDelta)
* Math.sin(lp.theta + laserAngles[angleIndex]);
pfree.x = pfree.x - phit.x;
pfree.y = pfree.y - phit.y;
IntPoint ipfree = map.world2map(pfree);
boolean found = false;
DoublePoint bestMu = new DoublePoint(0., 0.);
DoublePoint bestCell = new DoublePoint(0., 0.);
for (int xx = -kernelSize; xx <= kernelSize; xx++)
for (int yy = -kernelSize; yy <= kernelSize; yy++) {
IntPoint pr = new IntPoint(iphit.x + xx, iphit.y + yy);
IntPoint pf = new IntPoint(pr.x + ipfree.x, pr.y + ipfree.y);
PointAccumulator cell = (PointAccumulator) map.cell(pr, true);
PointAccumulator fcell = (PointAccumulator) map.cell(pf, true);
if (cell.doubleValue() > fullnessThreshold && fcell.doubleValue() < fullnessThreshold) {
DoublePoint mu = DoublePoint.minus(phit, cell.mean());
if (!found) {
bestMu = mu;
bestCell = cell.mean();
found = true;
} else if (DoublePoint.mulD(mu, mu) < DoublePoint.mulD(bestMu, bestMu)) {
bestMu = mu;
bestCell = cell.mean();
}
}
}
if (found) {
pairs.add(new DoublePointPair(phit, bestCell));
}
}
DoubleOrientedPoint result = new DoubleOrientedPoint(0.0, 0.0, 0.0);
LOG.error("result(" + pairs.size() + ")=" + result.x + " " + result.y + " " + result.theta);
pret.x = p.x + result.x;
pret.y = p.y + result.y;
pret.theta = p.theta + result.theta;
pret.theta = Math.atan2(Math.sin(pret.theta), Math.cos(pret.theta));
return score(map, p, readings);
}
public LikeliHoodAndScore likelihoodAndScore(
IGMap map, DoubleOrientedPoint p, double[] readings) {
double l = 0;
double s = 0;
int angleIndex = initialBeamsSkip;
DoubleOrientedPoint lp = new DoubleOrientedPoint(p.x, p.y, p.theta);
lp.x += Math.cos(p.theta) * laserPose.x - Math.sin(p.theta) * laserPose.y;
lp.y += Math.sin(p.theta) * laserPose.x + Math.cos(p.theta) * laserPose.y;
lp.theta += laserPose.theta;
double noHit = nullLikelihood / (likelihoodSigma);
int skip = 0;
int c = 0;
PointAccumulator emptyPointAccumulator = new PointAccumulator();
double freeDelta = map.getDelta() * freeCellRatio;
for (int rIndex = initialBeamsSkip; rIndex < readings.length; rIndex++, angleIndex++) {
skip++;
skip = skip > likelihoodSkip ? 0 : skip;
if (readings[rIndex] > usableRange) {
continue;
}
if (skip != 0) {
continue;
}
double phitx = lp.x;
double phity = lp.y;
phitx += readings[rIndex] * Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex]));
phity += readings[rIndex] * Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex]));
// IntPoint iphit = map.world2map(phitx, phity);
int iphitx = (int) Math.round((phitx - map.getCenter().x) / map.getDelta()) + map.getSizeX2();
int iphity = (int) Math.round((phity - map.getCenter().y) / map.getDelta()) + map.getSizeY2();
// DoublePoint pfree = new DoublePoint(lp.x, lp.y);
double pfreex = lp.x;
double pfreey = lp.y;
pfreex +=
(readings[rIndex] - freeDelta)
* Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex]));
pfreey +=
(readings[rIndex] - freeDelta)
* Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex]));
pfreex = pfreex - phitx;
pfreey = pfreey - phity;
// IntPoint ipfree = map.world2map(pfreex, pfreey);
int ipfreex =
(int) Math.round((pfreex - map.getCenter().x) / map.getDelta()) + map.getSizeX2();
int ipfreey =
(int) Math.round((pfreey - map.getCenter().y) / map.getDelta()) + map.getSizeY2();
boolean found = false;
// DoublePoint bestMu = new DoublePoint(0.0, 0.0);
double bestMux = 0.0;
double bestMuy = 0.0;
for (int xx = -kernelSize; xx <= kernelSize; xx++) {
for (int yy = -kernelSize; yy <= kernelSize; yy++) {
// IntPoint pr = new IntPoint(iphit.x + xx, iphit.y + yy);
int prx = iphitx + xx;
int pry = iphity + yy;
// IntPoint pf = new IntPoint(pr.x + ipfree.x, pr.y + ipfree.y);
int pfx = prx + ipfreex;
int pfy = pry + ipfreey;
// AccessibilityState s=map.storage().cellState(pr);
// if (s&Inside && s&Allocated){
PointAccumulator cell = (PointAccumulator) map.cell(prx, pry, true);
PointAccumulator fcell = (PointAccumulator) map.cell(pfx, pfy, true);
if (cell == null) {
cell = emptyPointAccumulator;
}
if (fcell == null) {
fcell = emptyPointAccumulator;
}
if (cell.doubleValue() > fullnessThreshold && fcell.doubleValue() < fullnessThreshold) {
double meanx = cell.accx / cell.n;
double meany = cell.accy / cell.n;
double mux = phitx - meanx;
double muy = phity - meany;
// DoublePoint mu = DoublePoint.minus(phit, cell.mean());
if (!found) {
bestMux = mux;
bestMuy = muy;
found = true;
} else {
double mulMu = mux * mux + muy * muy;
double mulBestMu = bestMux * bestMux + bestMuy * bestMuy;
if (mulMu < mulBestMu) {
bestMux = mux;
bestMuy = muy;
}
}
}
}
}
double mulBestMu = bestMux * bestMux + bestMuy * bestMuy;
if (found) {
s += Math.exp(-1.0 / gaussianSigma * mulBestMu);
c++;
}
double f = (1. / likelihoodSigma) * (mulBestMu);
l += (found) ? f : noHit;
}
return new LikeliHoodAndScore(s, l, c);
}
public LikeliHood likelihood(IGMap map, DoubleOrientedPoint p, double[] readings) {
List<ScoredMove> moveList = new ArrayList<ScoredMove>();
for (double xx = -llsamplerange; xx <= llsamplerange; xx += llsamplestep) {
for (double yy = -llsamplerange; yy <= llsamplerange; yy += llsamplestep) {
for (double tt = -lasamplerange; tt <= lasamplerange; tt += lasamplestep) {
DoubleOrientedPoint rp = new DoubleOrientedPoint(p.x, p.y, p.theta);
rp.x += xx;
rp.y += yy;
rp.theta += tt;
ScoredMove sm = new ScoredMove();
sm.pose = rp;
LikeliHoodAndScore ls = likelihoodAndScore(map, rp, readings);
sm.score = ls.s;
sm.likelihood = ls.l;
moveList.add(sm);
}
}
}
// normalize the likelihood
double lmax = -1e9;
double lcum = 0;
for (ScoredMove it : moveList) {
lmax = it.likelihood > lmax ? it.likelihood : lmax;
}
for (ScoredMove it : moveList) {
lcum += Math.exp(it.likelihood - lmax);
it.likelihood = Math.exp(it.likelihood - lmax);
}
DoubleOrientedPoint mean = new DoubleOrientedPoint(0.0, 0.0, 0.0);
for (ScoredMove it : moveList) {
double x = mean.x + it.pose.x * it.likelihood;
double y = mean.y + it.pose.y * it.likelihood;
double theta = mean.theta + it.pose.theta * it.likelihood;
mean.x = x;
mean.y = y;
mean.theta = theta;
}
mean.x = mean.x * (1. / lcum);
mean.y = mean.y * (1. / lcum);
mean.theta = mean.theta * (1. / lcum);
Covariance3 cov = new Covariance3(0., 0., 0., 0., 0., 0.);
for (ScoredMove it : moveList) {
DoubleOrientedPoint delta = DoubleOrientedPoint.minus(it.pose, mean);
delta.theta = Math.atan2(Math.sin(delta.theta), Math.cos(delta.theta));
cov.xx += delta.x * delta.x * it.likelihood;
cov.yy += delta.y * delta.y * it.likelihood;
cov.tt += delta.theta * delta.theta * it.likelihood;
cov.xy += delta.x * delta.y * it.likelihood;
cov.xt += delta.x * delta.theta * it.likelihood;
cov.yt += delta.y * delta.theta * it.likelihood;
}
cov.xx /= lcum;
cov.xy /= lcum;
cov.xt /= lcum;
cov.yy /= lcum;
cov.yt /= lcum;
cov.tt /= lcum;
return new LikeliHood(lmax, mean, cov, Math.log(lcum));
}
public double optimize(
DoubleOrientedPoint pnew, IGMap map, DoubleOrientedPoint init, double[] readings) {
double bestScore = -1;
DoubleOrientedPoint currentPose = new DoubleOrientedPoint(init.x, init.y, init.theta);
LikeliHoodAndScore likeliHoodAndScore = likelihoodAndScore(map, currentPose, readings);
// double currentScore = score(map, currentPose, readings);
double currentScore = likeliHoodAndScore.s;
double adelta = optAngularDelta, ldelta = optLinearDelta;
int refinement = 0;
int count = 0;
do {
if (bestScore >= currentScore || (Double.isNaN(bestScore) && Double.isNaN(currentScore))) {
refinement++;
adelta *= .5;
ldelta *= .5;
}
bestScore = currentScore;
DoubleOrientedPoint bestLocalPose =
new DoubleOrientedPoint(currentPose.x, currentPose.y, currentPose.theta);
DoubleOrientedPoint localPose;
Move move = Move.Front;
do {
localPose = new DoubleOrientedPoint(currentPose.x, currentPose.y, currentPose.theta);
switch (move) {
case Front:
localPose.x += ldelta;
move = Move.Back;
break;
case Back:
localPose.x -= ldelta;
move = Move.Left;
break;
case Left:
localPose.y -= ldelta;
move = Move.Right;
break;
case Right:
localPose.y += ldelta;
move = Move.TurnLeft;
break;
case TurnLeft:
localPose.theta += adelta;
move = Move.TurnRight;
break;
case TurnRight:
localPose.theta -= adelta;
move = Move.Done;
break;
default:
}
localPose.theta = Utils.theta(localPose.theta);
double odo_gain = 1;
if (angularOdometryReliability > 0.) {
double dth = Utils.theta(init.theta - localPose.theta);
dth = Math.atan2(Math.sin(dth), Math.cos(dth));
dth *= dth;
odo_gain *= Math.exp(-angularOdometryReliability * dth);
}
if (linearOdometryReliability > 0.) {
double dx = init.x - localPose.x;
double dy = init.y - localPose.y;
double drho = dx * dx + dy * dy;
odo_gain *= Math.exp(-linearOdometryReliability * drho);
}
likeliHoodAndScore = likelihoodAndScore(map, localPose, readings);
count++;
if (count == 50) {
move = Move.Done;
}
// double localScore = odo_gain * score(map, localPose, readings);
double localScore = odo_gain * likeliHoodAndScore.s;
if (localScore > currentScore) {
currentScore = localScore;
bestLocalPose = new DoubleOrientedPoint(localPose.x, localPose.y, localPose.theta);
}
} while (move != Move.Done);
currentPose = new DoubleOrientedPoint(bestLocalPose.x, bestLocalPose.y, bestLocalPose.theta);
} while ((currentScore > bestScore || refinement <= optRecursiveIterations) && cout < 50);
pnew.x = currentPose.x;
pnew.y = currentPose.y;
pnew.theta = currentPose.theta;
// System.out.println("Iteration count: " + count);
return bestScore;
}
public double optimize(
DoubleOrientedPoint _mean,
Covariance3 _cov,
IGMap map,
DoubleOrientedPoint init,
double[] readings) {
List<ScoredMove> moveList = new ArrayList<ScoredMove>();
double bestScore = -1;
DoubleOrientedPoint currentPose = init;
ScoredMove sm = new ScoredMove(currentPose, 0, 0);
LikeliHoodAndScore ls = likelihoodAndScore(map, currentPose, readings);
sm.likelihood = ls.l;
sm.score = ls.s;
double currentScore = sm.score;
moveList.add(sm);
double adelta = optAngularDelta, ldelta = optLinearDelta;
int refinement = 0;
do {
if (bestScore >= currentScore) {
refinement++;
adelta *= .5;
ldelta *= .5;
}
bestScore = currentScore;
DoubleOrientedPoint bestLocalPose = currentPose;
DoubleOrientedPoint localPose = currentPose;
Move move = Move.Front;
do {
localPose = currentPose;
switch (move) {
case Front:
localPose.x += ldelta;
move = Move.Back;
break;
case Back:
localPose.x -= ldelta;
move = Move.Left;
break;
case Left:
localPose.y -= ldelta;
move = Move.Right;
break;
case Right:
localPose.y += ldelta;
move = Move.TurnLeft;
break;
case TurnLeft:
localPose.theta += adelta;
move = Move.TurnRight;
break;
case TurnRight:
localPose.theta -= adelta;
move = Move.Done;
break;
default:
}
double localScore = 0, localLikelihood = 0;
// update the score
ls = likelihoodAndScore(map, localPose, readings);
localLikelihood = ls.s;
localLikelihood = ls.l;
if (localScore > currentScore) {
currentScore = localScore;
bestLocalPose = localPose;
}
sm.score = localScore;
sm.likelihood = localLikelihood;
sm.pose = localPose;
moveList.add(sm);
// update the move list
} while (move != Move.Done);
currentPose = bestLocalPose;
// here we look for the best move;
} while (currentScore > bestScore || refinement < optRecursiveIterations);
// normalize the likelihood
double lmin = 1e9;
double lmax = -1e9;
for (ScoredMove it : moveList) {
lmin = it.likelihood < lmin ? it.likelihood : lmin;
lmax = it.likelihood > lmax ? it.likelihood : lmax;
}
for (ScoredMove it : moveList) {
it.likelihood = Math.exp(it.likelihood - lmax);
}
// compute the mean
DoubleOrientedPoint mean = new DoubleOrientedPoint(0.0, 0.0, 0.0);
double lacc = 0;
for (ScoredMove it : moveList) {
mean = DoubleOrientedPoint.plus(mean, DoubleOrientedPoint.mulN(it.pose, it.likelihood));
lacc += it.likelihood;
}
mean = DoubleOrientedPoint.mulN(mean, (1. / lacc));
Covariance3 cov = new Covariance3(0., 0., 0., 0., 0., 0.);
for (ScoredMove it : moveList) {
DoubleOrientedPoint delta = DoubleOrientedPoint.minus(it.pose, mean);
delta.theta = Math.atan2(Math.sin(delta.theta), Math.cos(delta.theta));
cov.xx += delta.x * delta.x * it.likelihood;
cov.yy += delta.y * delta.y * it.likelihood;
cov.tt += delta.theta * delta.theta * it.likelihood;
cov.xy += delta.x * delta.y * it.likelihood;
cov.xt += delta.x * delta.theta * it.likelihood;
cov.yt += delta.y * delta.theta * it.likelihood;
}
cov.xx /= lacc;
cov.xy /= lacc;
cov.xt /= lacc;
cov.yy /= lacc;
cov.yt /= lacc;
cov.tt /= lacc;
_mean = currentPose;
_cov = cov;
return bestScore;
}
public double registerScan(IGMap map, DoubleOrientedPoint p, double[] readings) {
if (!m_activeAreaComputed) computeActiveArea(map, p, readings);
// this operation replicates the cells that will be changed in the registration operation
// map.allocActiveArea();
DoubleOrientedPoint lp = new DoubleOrientedPoint(p.x, p.y, p.theta);
lp.x += Math.cos(p.theta) * laserPose.x - Math.sin(p.theta) * laserPose.y;
lp.y += Math.sin(p.theta) * laserPose.x + Math.cos(p.theta) * laserPose.y;
lp.theta += laserPose.theta;
IntPoint p0 = map.world2map(lp);
int readingIndex;
int angleIndex = initialBeamsSkip;
double esum = 0;
for (readingIndex = initialBeamsSkip; readingIndex < laserBeams; readingIndex++, angleIndex++) {
if (generateMap) {
double d = readings[readingIndex];
if (d >= laserMaxRange) continue;
if (d >= usableRange) d = usableRange;
DoublePoint phit =
new DoublePoint(
d * Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex])) + lp.x,
d * Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex])) + lp.y);
IntPoint p1 = map.world2map(phit);
d += map.getDelta();
GridLineTraversalLine line = new GridLineTraversalLine();
line.points = m_linePoints;
GridLineTraversalLine.gridLine(p0, p1, line);
for (int i = 0; i < line.numPoints - 1; i++) {
PointAccumulator pa = (PointAccumulator) map.cell(line.points[i], false);
if (pa != null) {
double e = -pa.entropy();
pa.update(false, new DoublePoint(0.0, 0.0));
e += pa.entropy();
esum += e;
}
}
if (d < usableRange) {
PointAccumulator pa = (PointAccumulator) map.cell(p1, false);
if (pa != null) {
double e = -pa.entropy();
pa.update(true, phit);
e += pa.entropy();
esum += e;
}
}
} else {
double r = readings[readingIndex];
if (r >= laserMaxRange || r >= usableRange || r == 0) {
continue;
}
// DoublePoint phit = new DoublePoint(lp.x, lp.y);
double phitx = lp.x;
double phity = lp.y;
phitx += r * Math.cos(Utils.theta(lp.theta + laserAngles[angleIndex]));
phity += r * Math.sin(Utils.theta(lp.theta + laserAngles[angleIndex]));
IntPoint p1 = map.world2map(phitx, phity);
PointAccumulator pa = (PointAccumulator) map.cell(p1, false);
if (pa != null) {
pa.update(true, phitx, phity);
}
}
}
return esum;
}
public void computeActiveArea(IGMap map, DoubleOrientedPoint p, double[] readings) {
if (m_activeAreaComputed) return;
// Set<IntPoint> activeArea = new HashSet<IntPoint>();
DoubleOrientedPoint lp = new DoubleOrientedPoint(p.x, p.y, p.theta);
lp.x += Math.cos(p.theta) * laserPose.x - Math.sin(p.theta) * laserPose.y;
lp.y += Math.sin(p.theta) * laserPose.x + Math.cos(p.theta) * laserPose.y;
lp.theta += laserPose.theta;
// IntPoint p0 = map.world2map(lp);
DoublePoint min = map.map2world(new IntPoint(0, 0));
DoublePoint max = map.map2world(new IntPoint(map.getMapSizeX() - 1, map.getMapSizeY() - 1));
if (lp.x < min.x) min.x = lp.x;
if (lp.y < min.y) min.y = lp.y;
if (lp.x > max.x) max.x = lp.x;
if (lp.y > max.y) max.y = lp.y;
int readingIndex;
int angleIndex = initialBeamsSkip;
for (readingIndex = initialBeamsSkip; readingIndex < laserBeams; readingIndex++, angleIndex++) {
double r = readings[readingIndex];
double angle = laserAngles[angleIndex];
if (r > laserMaxRange || r == 0.0 || r > Double.MAX_VALUE) continue;
double d = r > usableRange ? usableRange : r;
// DoublePoint phit = new DoublePoint(lp.x, lp.y);
double phitx = lp.x, phity = lp.y;
phitx += d * Math.cos(lp.theta + angle);
phity += d * Math.sin(lp.theta + angle);
if (phitx < min.x) min.x = phitx;
if (phity < min.y) min.y = phity;
if (phitx > max.x) max.x = phitx;
if (phity > max.y) max.y = phity;
}
if (!map.isInsideD(min) || !map.isInsideD(max)) {
DoublePoint lmin = map.map2world(new IntPoint(0, 0));
DoublePoint lmax = map.map2world(new IntPoint(map.getMapSizeX() - 1, map.getMapSizeY() - 1));
min.x = (min.x >= lmin.x) ? lmin.x : min.x - enlargeStep;
max.x = (max.x <= lmax.x) ? lmax.x : max.x + enlargeStep;
min.y = (min.y >= lmin.y) ? lmin.y : min.y - enlargeStep;
max.y = (max.y <= lmax.y) ? lmax.y : max.y + enlargeStep;
map.resize(min.x, min.y, max.x, max.y);
}
// readingIndex = initialBeamsSkip;
// angleIndex = initialBeamsSkip;
// for (readingIndex = initialBeamsSkip; readingIndex < laserBeams; readingIndex++,
// angleIndex++) {
// if (generateMap) {
// double d = readings[readingIndex];
// if (d > laserMaxRange || d == 0.0 || d > Double.MAX_VALUE)
// continue;
// if (d > usableRange)
// d = usableRange;
//
// DoublePoint phit = new DoublePoint(d * Math.cos(lp.theta +
// laserAngles[angleIndex]) + lp.x, d * Math.sin(lp.theta + laserAngles[angleIndex]) + lp.y);
// p0 = map.world2map(lp);
// IntPoint p1 = map.world2map(phit);
//
// d += map.getDelta();
// GridLineTraversalLine line = new GridLineTraversalLine();
// line.points = m_linePoints;
// GridLineTraversalLine.gridLine(p0, p1, line);
// for (int i = 0; i < line.numPoints - 1; i++) {
// IntPoint patch = (m_linePoints[i]);
// activeArea.add(patch);
// }
// if (d <= usableRange) {
// IntPoint patch = (p1);
// activeArea.add(patch);
// }
// } else {
// double r = readings[readingIndex];
// double angle = laserAngles[angleIndex];
// if (readings[readingIndex] > laserMaxRange || readings[readingIndex] >
// usableRange) {
// continue;
// }
//// DoublePoint phit = new DoublePoint(lp.x, lp.y);
// double phitx = lp.x;
// double phity = lp.y;
// phitx += r * Math.cos(lp.theta + angle);
// phity += r * Math.sin(lp.theta + angle);
//
// IntPoint p1 = map.world2map(phitx, phity);
// assert (p1.x >= 0 && p1.y >= 0);
// IntPoint cp = (p1);
// assert (cp.x >= 0 && cp.y >= 0);
// activeArea.add(cp);
// }
// }
// this allocates the unallocated cells in the active area of the map
// map.setActiveArea(activeArea, false);
m_activeAreaComputed = true;
}