/// <summary>
/// Construct a PHDNavigator using the indicated vehicle as a reference.
/// </summary>
/// <param name="vehicle">Vehicle to track.</param>
/// <param name="onlymapping">If true, don't do SLAM, but mapping
/// (i.e. the localization is assumed known). Currently not used.</param>
public ISAM2Navigator(Vehicle <MeasurerT, PoseT, MeasurementT> vehicle, bool onlymapping = false)
: base(vehicle, onlymapping)
{
int nmeasurement = vehicle.MeasurementCovariance.Length;
int nmotion = vehicle.MotionCovariance.Length;
double[] measurementnoise = new double[nmeasurement];
double[] motionnoise = new double[nmotion];
double[] mparams = vehicle.Measurer.ToLinear();
// NOTE the iSAM2 interface assumes independent noise in each component,
// discarding every entry that's not in the diagonal;
// although it is possible to define a complete matrix,
// it doesn't seem to be worth it when comparing performance
for (int i = 0; i < nmeasurement; ++i)
{
measurementnoise[i] = Math.Sqrt(vehicle.MeasurementCovariance[i][i]);
}
for (int i = 0; i < nmotion; ++i)
{
motionnoise[i] = MotionDt * Math.Sqrt(vehicle.MotionCovariance[i][i]);
}
bestestimate = new TrackVehicle <MeasurerT, PoseT, MeasurementT>();
MapModel = new IndexedMap(3);
plmodel = new IndexedMap(3);
CandidateMapModel = new IndexedMap(3);
BestEstimate.Pose = vehicle.Pose.DClone();
handle = NewNavigator(vehicle.Pose.State, measurementnoise, motionnoise, mparams);
nextlabel = 0;
}
/// <summary>
/// Retrieve the submap of all the landmarks that match a condition.
/// </summary>
/// <param name="match">Match condition.</param>
/// <returns>Filtered submap.</returns>
public IMap FindAll(Predicate <Gaussian> match)
{
IndexedMap filtered = new IndexedMap(Dimensions);
filtered.landmarks = this.landmarks.FindAll(match);
return(filtered);
}
/// <summary>
/// Get the map estimate.
/// </summary>
/// <param name="plmodel">Side model that contains the covariances of the
/// pose-landmark pairs projected into the measurement space.
/// Takes the form E = JSJ^T + R, where
/// J is the measurement jacobian in both the pose and landmark directions,
/// S is the joint covariance of the best estimate current pose and the landmark and
/// R is the measurement noise.
/// </param>
/// <returns>Map estimate as points with certain covariance (gaussians).</returns>
private unsafe IndexedMap GetMapModel(out IndexedMap plmodel)
{
int length;
Gaussian component;
IndexedMap mapmodel = new IndexedMap(3);
plmodel = new IndexedMap(3);
double *ptrmapmodel = (double *)ISAM2Lib.getmapmodel(handle, out length);
double *ptrmapcov = (double *)ISAM2Lib.getmapcovariances(handle, out length);
double *ptrplcov = (double *)ISAM2Lib.getplcovariances(handle, out length);
// main model: mean and covariance
for (int i = 0, k = 0, h = 0; i < length; i++, k += 3, h += 9)
{
double[] mean = new double[3];
double[][] covariance = { new double[3], new double[3], new double[3] };
mean[0] = ptrmapmodel[k + 0];
mean[1] = ptrmapmodel[k + 1];
mean[2] = ptrmapmodel[k + 2];
covariance[0][0] = ptrmapcov[h + 0];
covariance[0][1] = ptrmapcov[h + 1];
covariance[0][2] = ptrmapcov[h + 2];
covariance[1][0] = ptrmapcov[h + 3];
covariance[1][1] = ptrmapcov[h + 4];
covariance[1][2] = ptrmapcov[h + 5];
covariance[2][0] = ptrmapcov[h + 6];
covariance[2][1] = ptrmapcov[h + 7];
covariance[2][2] = ptrmapcov[h + 8];
component = new Gaussian(mean, covariance, 1.0);
mapmodel.Add(component);
}
// side model: covariance projected into measurement space
// includes pose uncertainty and measurement noise
for (int i = 0, h = 0; i < length; i++, h += 9)
{
double[][] covariance = { new double[3], new double[3], new double[3] };
covariance[0][0] = ptrplcov[h + 0];
covariance[0][1] = ptrplcov[h + 1];
covariance[0][2] = ptrplcov[h + 2];
covariance[1][0] = ptrplcov[h + 3];
covariance[1][1] = ptrplcov[h + 4];
covariance[1][2] = ptrplcov[h + 5];
covariance[2][0] = ptrplcov[h + 6];
covariance[2][1] = ptrplcov[h + 7];
covariance[2][2] = ptrplcov[h + 8];
component = new Gaussian(new double[3], covariance, 1.0);
plmodel.Add(component);
}
return(mapmodel);
}
/// <summary>
/// Find the data association labels from the new valid measurements and
/// the internal previous map model using Mahalanobis association.
/// </summary>
/// <param name="measurements">New measurements.</param>
/// <returns>Association labels.</returns>
public List <int> FindLabels(List <MeasurementT> measurements)
{
if (DAAlgorithm == DataAssociationAlgorithm.Perfect)
{
if (!RefVehicle.HasDataAssociation)
{
var exception = new InvalidOperationException("Tried to use perfect data association when none exists.");
exception.Data["module"] = "association";
throw exception;
}
return(RefVehicle.DataAssociation);
}
double[][] I = 0.001.Multiply(Accord.Math.Matrix.Identity(3).ToArray());
bool[] keepcandidate = new bool[CandidateMapModel.Count];
double[][] R;
Gaussian[] q;
Gaussian[] qcandidate;
var pose = BestEstimate;
IndexedMap visible = new IndexedMap(3);
List <int> visibleLandmarks = new List <int>();
for (int i = 0; i < MapModel.Count; i++)
{
if (pose.Visible(MapModel[i].Mean))
{
visible.Add(MapModel[i]);
visibleLandmarks.Add(i);
}
}
double logPD = Math.Log(pose.PD);
double logclutter = Math.Log(pose.ClutterDensity);
int n = visible.Count + CandidateMapModel.Count;
int m = visible.Count + CandidateMapModel.Count + measurements.Count;
// distances(i, k) = distance between landmark i and measurements k
SparseMatrix distances = new SparseMatrix(n, n, double.NegativeInfinity);
int candidatecount = CandidateMapModel.Count;
// candidate count at the beggining of the process
// this is so the measurements aren't compared with other measurements
// (if one gets promoted to candidate, the next one could think it's similar to it
// but that isn't sensible: one landmark -> hopefully one measurement)
R = pose.MeasurementCovariance;
q = new Gaussian[visible.Count];
qcandidate = new Gaussian[CandidateMapModel.Count];
for (int i = 0; i < q.Length; i++)
{
Gaussian component = visible[i];
if (DAAlgorithm == DataAssociationAlgorithm.Mahalanobis)
{
q[i] = new Gaussian(pose.Measurer.MeasurePerfect(pose.Pose, component.Mean).ToLinear(),
plmodel[visibleLandmarks[i]].Covariance,
1.0);
}
else
{
q[i] = new Gaussian(component.Mean, I, 1.0);
}
}
for (int i = 0; i < qcandidate.Length; i++)
{
Gaussian component = CandidateMapModel[i];
if (DAAlgorithm == DataAssociationAlgorithm.Mahalanobis)
{
// assume the covariance is zero, since there's nothing better to assume here
// note that this is more stringent on the unproven data, as they are given
// less leeway for noise than the already associated landmark
qcandidate[i] = new Gaussian(pose.Measurer.MeasurePerfect(pose.Pose, component.Mean).ToLinear(),
R, component.Weight);
}
else
{
qcandidate[i] = new Gaussian(component.Mean, I, 1.0);
}
}
Gaussian[] vlandmarks = q.Concatenate(qcandidate);
for (int i = 0; i < n; i++)
{
for (int k = 0; k < measurements.Count; k++)
{
double distance2;
if (DAAlgorithm == DataAssociationAlgorithm.Mahalanobis)
{
distance2 = vlandmarks[i].SquareMahalanobis(measurements[k].ToLinear());
}
else
{
distance2 = vlandmarks[i].Mean.SquareEuclidean(pose.Measurer.MeasureToMap(pose.Pose, measurements[k]));
}
if (distance2 < MatchThreshold * MatchThreshold)
{
distances[i, k] = logPD + Math.Log(vlandmarks[i].Multiplier) - 0.5 * distance2;
}
}
}
for (int i = 0; i < vlandmarks.Length; i++)
{
distances[i, measurements.Count + i] = logPD;
}
for (int i = 0; i < measurements.Count; i++)
{
distances[vlandmarks.Length + i, i] = logclutter;
}
// fill the (Misdetection x Clutter) quadrant of the matrix with zeros (don't contribute)
for (int i = vlandmarks.Length; i < m; i++)
{
for (int k = measurements.Count; k < m; k++)
{
distances[i, k] = 0;
}
}
int[] assignments = GraphCombinatorics.LinearAssignment(distances);
// the assignment vector after removing all the clutter variables
List <int> labels = new List <int>();
for (int i = 0; i < measurements.Count; i++)
{
labels.Add(int.MinValue);
}
// proved landmark
for (int i = 0; i < visible.Count; i++)
{
if (assignments[i] < measurements.Count)
{
labels[assignments[i]] = visibleLandmarks[i];
}
}
// candidate landmark
for (int i = visible.Count; i < vlandmarks.Length; i++)
{
if (assignments[i] < measurements.Count)
{
int k = i - visible.Count;
labels[assignments[i]] = -k - 1;
// negative labels are for candidates,
// note that zero is already occupied by the associated landmarks
// improve the estimated landmark by averaging with the new measurement
double w = CandidateMapModel[k].Weight;
CandidateMapModel[k] =
new Gaussian((CandidateMapModel[k].Mean.Multiply(w).Add(
BestEstimate.Measurer.MeasureToMap(BestEstimate.Pose, measurements[assignments[i]]))).Divide(w + 1),
I,
w + 1);
// note the comparison between double and int
// since the weight is only used with integer values (and addition by one)
// there should never be any truncation error, at least while
// the number has less than 23/52 bits (for float/double);
// this amounts to 8388608 and 4.5e15 so it should be always ok.
// In fact, the gtsam key system fails before that
// (it uses three bytes for numbering, one for character)
if (CandidateMapModel[k].Weight >= NewLandmarkThreshold)
{
labels[assignments[i]] = NextLabel;
}
else
{
keepcandidate[k] = true;
// only keep candidates that haven't been added, but are still visible
}
}
}
// else: unmatched measurements, add to candidates
for (int i = 0; i < measurements.Count; i++)
{
if (labels[i] == int.MinValue)
{
// if far from everything, generate a new candidate at the measured point
// note that the covariance is assumed zero, though that would throw an exception,
// as it doesn't have an inverse, so the identity is used instead (dummy value)
CandidateMapModel.Add(new Gaussian(BestEstimate.Measurer.MeasureToMap(BestEstimate.Pose, measurements[i]), I, 1));
if (NewLandmarkThreshold <= 1)
{
CandidateMapModel.RemoveAt(CandidateMapModel.Count - 1);
labels[i] = NextLabel;
}
}
}
// anything that wasn't seen goes away, it was clutter
for (int i = keepcandidate.Length - 1; i >= 0; i--)
{
if (!keepcandidate[i])
{
CandidateMapModel.RemoveAt(i);
}
}
return(labels);
}