Package com.irurueta.geometry.estimators

Class Point2DRobustEstimator

java.lang.Object
com.irurueta.geometry.estimators.Point2DRobustEstimator
Direct Known Subclasses:
LMedSPoint2DRobustEstimator, MSACPoint2DRobustEstimator, PROMedSPoint2DRobustEstimator, PROSACPoint2DRobustEstimator, RANSACPoint2DRobustEstimator
public abstract class Point2DRobustEstimator extends Object
This is an abstract class for algorithms to robustly find the best 3D point that intersects in a collection of 2D lines. Implementations of this class should be able to detect and discard outliers in order to find the best solution.

  • Field Details

    • MINIMUM_SIZE

      public static final int MINIMUM_SIZE
      Minimum number of 2D lines required to estimate a point.
      See Also:

    • DEFAULT_PROGRESS_DELTA

      public static final float DEFAULT_PROGRESS_DELTA
      Default amount of progress variation before notifying a change in estimation progress. By default, this is set to 5%.
      See Also:

    • MIN_PROGRESS_DELTA

      public static final float MIN_PROGRESS_DELTA
      Minimum allowed value for progress delta.
      See Also:

    • MAX_PROGRESS_DELTA

      public static final float MAX_PROGRESS_DELTA
      Maximum allowed value for progress delta.
      See Also:

    • DEFAULT_CONFIDENCE

      public static final double DEFAULT_CONFIDENCE
      Constant defining default confidence of the estimated result, which is 99%. This means that with a probability of 99% estimation will be accurate because chosen sub-samples will be inliers.
      See Also:

    • DEFAULT_MAX_ITERATIONS

      public static final int DEFAULT_MAX_ITERATIONS
      Default maximum allowed number of iterations.
      See Also:

    • MIN_CONFIDENCE

      public static final double MIN_CONFIDENCE
      Minimum allowed confidence value.
      See Also:

    • MAX_CONFIDENCE

      public static final double MAX_CONFIDENCE
      Maximum allowed confidence value.
      See Also:

    • MIN_ITERATIONS

      public static final int MIN_ITERATIONS
      Minimum allowed number of iterations.
      See Also:

    • DEFAULT_ROBUST_METHOD

      public static final com.irurueta.numerical.robust.RobustEstimatorMethod DEFAULT_ROBUST_METHOD
      Default robust estimator method when none is provided.

    • DEFAULT_REFINE_RESULT

      public static final boolean DEFAULT_REFINE_RESULT
      Indicates that result is refined by default using Levenberg-Marquardt fitting algorithm over found inliers.
      See Also:

    • DEFAULT_KEEP_COVARIANCE

      public static final boolean DEFAULT_KEEP_COVARIANCE
      Indicates that covariance is not kept by default after refining result.
      See Also:

    • listener

      protected Point2DRobustEstimatorListener listener
      Listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

    • locked

      protected volatile boolean locked
      Indicates if this estimator is locked because an estimation is being computed.

    • progressDelta

      protected float progressDelta
      Amount of progress variation before notifying a progress change during estimation.

    • confidence

      protected double confidence
      Amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.

    • maxIterations

      protected int maxIterations
      Maximum allowed number of iterations. When the maximum number of iterations is exceeded, result will not be available, however an approximate result will be available for retrieval.

    • lines

      protected List<Line2D> lines
      List of lines to be used to estimate a 2D point. Provided list must have a size greater or equal than MINIMUM_SIZE.

    • inliersData

      protected com.irurueta.numerical.robust.InliersData inliersData
      Data related to inliers found after estimation.

    • refineResult

      protected boolean refineResult
      Indicates whether result must be refined using Levenberg-Marquardt fitting algorithm over found inliers. If true, inliers will be computed and kept in any implementation regardless of the settings.

    • refinementCoordinatesType

      private CoordinatesType refinementCoordinatesType
      Coordinates type to use for refinement. When using inhomogeneous coordinates a 3x3 covariance matrix is estimated. When using homogeneous coordinates a 4x4 covariance matrix is estimated.

    • keepCovariance

      private boolean keepCovariance
      Indicates whether covariance must be kept after refining result. This setting is only taken into account if result is refined.

    • covariance

      private com.irurueta.algebra.Matrix covariance
      Estimated covariance of estimated 2D point. This is only available when result has been refined and covariance is kept.

  • Constructor Details

    • Point2DRobustEstimator

      protected Point2DRobustEstimator()
      Constructor.

    • Point2DRobustEstimator

      protected Point2DRobustEstimator(Point2DRobustEstimatorListener listener)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

    • Point2DRobustEstimator

      protected Point2DRobustEstimator(List<Line2D> lines)
      Constructor with lines.
      Parameters:
      lines - 2D lines to estimate a 2D point.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • Point2DRobustEstimator

      protected Point2DRobustEstimator(Point2DRobustEstimatorListener listener, List<Line2D> lines)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a 2D point.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

  • Method Details

    • getListener

      public Point2DRobustEstimatorListener getListener()
      Returns reference to listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      Returns:
      listener to be notified of events.

    • setListener

      public void setListener(Point2DRobustEstimatorListener listener) throws LockedException
      Sets listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      Parameters:
      listener - listener to be notified of events.
      Throws:
      LockedException - if robust estimator is locked.

    • isListenerAvailable

      public boolean isListenerAvailable()
      Indicates whether listener has been provided and is available for retrieval.
      Returns:
      true if available, false otherwise.

    • isLocked

      public boolean isLocked()
      Indicates if this instance is locked because estimation is being computed.
      Returns:
      true if locked, false otherwise.

    • getProgressDelta

      public float getProgressDelta()
      Returns amount of progress variation before notifying a progress change during estimation.
      Returns:
      amount of progress variation before notifying a progress change during estimation.

    • setProgressDelta

      public void setProgressDelta(float progressDelta) throws LockedException
      Sets amount of progress variation before notifying a progress change during estimation.
      Parameters:
      progressDelta - amount of progress variation before notifying a progress change during estimation.
      Throws:
      IllegalArgumentException - if progress delta is less than zero or greater than 1.
      LockedException - if this estimator is locked because an estimation is being computed.

    • getConfidence

      public double getConfidence()
      Returns amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.
      Returns:
      amount of confidence as a value between 0.0 and 1.0.

    • setConfidence

      public void setConfidence(double confidence) throws LockedException
      Sets amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.
      Parameters:
      confidence - confidence to be set as a value between 0.0 and 1.0.
      Throws:
      IllegalArgumentException - if provided value is not between 0.0 and 1.0.
      LockedException - if this estimator is locked because an estimator is being computed.

    • getMaxIterations

      public int getMaxIterations()
      Returns maximum allowed number of iterations. If maximum allowed number of iterations is achieved without converging to a result when calling estimate(), a RobustEstimatorException will be raised.
      Returns:
      maximum allowed number of iterations.

    • setMaxIterations

      public void setMaxIterations(int maxIterations) throws LockedException
      Sets maximum allowed number of iterations. When the maximum number of iterations is exceeded, result will not be available, however an approximate result will be available for retrieval.
      Parameters:
      maxIterations - maximum allowed number of iterations to be set.
      Throws:
      IllegalArgumentException - if provided value is less than 1.
      LockedException - if this estimator is locked because an estimation is being computed.

    • getInliersData

      public com.irurueta.numerical.robust.InliersData getInliersData()
      Gets data related to inliers found after estimation.
      Returns:
      data related to inliers found after estimation.

    • isResultRefined

      public boolean isResultRefined()
      Indicates whether result must be refined using Levenberg-Marquardt fitting algorithm over found inliers. If true, inliers will be computed and kept in any implementation regardless of the settings.
      Returns:
      true to refine result, false to simply use result found by robust estimator without further refining.

    • setResultRefined

      public void setResultRefined(boolean refineResult) throws LockedException
      Specifies whether result must be refined using Levenberg-Marquardt fitting algorithm over found inliers.
      Parameters:
      refineResult - true to refine result, false to simply use result found by robust estimator without further refining.
      Throws:
      LockedException - if estimator is locked.

    • getRefinementCoordinatesType

      public CoordinatesType getRefinementCoordinatesType()
      Gets coordinates type to use for refinement. When using inhomogeneous coordinates a 3x3 covariance matrix is estimated. When using homogeneous coordinates a 4x4 covariance matrix is estimated.
      Returns:
      coordinates type to use for refinement.

    • setRefinementCoordinatesType

      public void setRefinementCoordinatesType(CoordinatesType refinementCoordinatesType) throws LockedException
      Sets coordinates type to use for refinement. When using inhomogeneous coordinates a 3x3 covariance matrix is estimated. When using homogeneous coordinates a 4x4 covariance matrix is estimated.
      Parameters:
      refinementCoordinatesType - coordinates type to use for refinement.
      Throws:
      LockedException - if estimator is locked.

    • isCovarianceKept

      public boolean isCovarianceKept()
      Indicates whether covariance must be kept after refining result. This setting is only taken into account if result is refined.
      Returns:
      true if covariance must be kept after refining result, false otherwise.

    • setCovarianceKept

      public void setCovarianceKept(boolean keepCovariance) throws LockedException
      Specifies whether covariance must be kept after refining result. This setting is only taken into account if result is refined.
      Parameters:
      keepCovariance - true if covariance must be kept after refining result, false otherwise.
      Throws:
      LockedException - if estimator is locked.

    • getLines

      public List<Line2D> getLines()
      Returns list of lines to be used to estimate a 2D point. Provided list must have a size greater or equal than MINIMUM_SIZE
      Returns:
      list of lines to be used to estimate a 2D point.

    • setLines

      public void setLines(List<Line2D> lines) throws LockedException
      Sets list of lines to be used to estimate a 2D point. Provided list must have a size greater or equal than MINIMUM_SIZE.
      Parameters:
      lines - list of lines to be used to estimate a 2D point.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.
      LockedException - if estimator is locked because a computation is already in progress.

    • isReady

      public boolean isReady()
      Indicates if estimator is ready to start the 2D point estimation. This is true when a minimum if MINIMUM_SIZE lines are available.
      Returns:
      true if estimator is ready, false otherwise.

    • getQualityScores

      public double[] getQualityScores()
      Returns quality scores corresponding to each line. The larger the score value the better the quality of the line measure. This implementation always returns null. Subclasses using quality scores must implement proper behaviour.
      Returns:
      quality scores corresponding to each point.

    • setQualityScores

      public void setQualityScores(double[] qualityScores) throws LockedException
      Sets quality scores corresponding to each line. The larger the score value the better the quality of the line measure. This implementation makes no action. Subclasses using quality scores must implement proper behaviour.
      Parameters:
      qualityScores - quality scores corresponding to each sampled line.
      Throws:
      LockedException - if robust estimator is locked because an estimation is already in progress.
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 2 samples).

    • getCovariance

      public com.irurueta.algebra.Matrix getCovariance()
      Gets estimated covariance of estimated 3D point if available. This is only available when result has been refined and covariance is kept.
      Returns:
      estimated covariance or null.

    • create

      public static Point2DRobustEstimator create(com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided robust estimator method.
      Parameters:
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.

    • create

      public static Point2DRobustEstimator create(List<Line2D> lines, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided lines and robust estimator method.
      Parameters:
      lines - 2D lines to estimate a 2D point.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, List<Line2D> lines, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and lines.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a 2D point.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(double[] qualityScores, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided robust estimator method.
      Parameters:
      qualityScores - quality scores corresponding to each provided line.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 2 lines).

    • create

      public static Point2DRobustEstimator create(List<Line2D> lines, double[] qualityScores, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided lines and robust estimator method.
      Parameters:
      lines - 2D lines to estimate a 2D point.
      qualityScores - quality scores corresponding to each provided line.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have the same size as the list of provided quality scores, or it their size is not greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, double[] qualityScores, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      qualityScores - quality scores corresponding to each provided line.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 2 lines).

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, List<Line2D> lines, double[] qualityScores, com.irurueta.numerical.robust.RobustEstimatorMethod method)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and lines.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a 2D point.
      qualityScores - quality scores corresponding to each provided point.
      method - method of a robust estimator algorithm to estimate the best 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have the same size as the list of provided quality scores, or it their size is not greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create()
      Creates a 2D point robust estimator based on 2D line samples and using default robust estimator method.
      Returns:
      an instance of a 2D point robust estimator.

    • create

      public static Point2DRobustEstimator create(List<Line2D> lines)
      Creates a 2D point robust estimator based on 2D line samples and using provided lines and default robust estimator method.
      Parameters:
      lines - 2D lines to estimate a 2D point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and default robust estimator method.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      Returns:
      an instance of a 2D point robust estimator.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, List<Line2D> lines)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and lines and default robust estimator method.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a point.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(double[] qualityScores)
      Creates a 2D point robust estimator based on 2D line samples and using default robust estimator method.
      Parameters:
      qualityScores - quality scores corresponding to each provided line
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 2 lines).

    • create

      public static Point2DRobustEstimator create(List<Line2D> lines, double[] qualityScores)
      Creates a 2D point robust estimator based on 2D line samples and using provided lines and default estimator method.
      Parameters:
      lines - 2D lines to estimate a 2D point.
      qualityScores - quality scores corresponding to each provided line.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have the same size as the list of provided quality scores, or if their size is not greater or equal than MINIMUM_SIZE.

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, double[] qualityScores)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and default estimator method.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      qualityScores - quality scores corresponding to each provided line
      Returns:
      an instance of a circle robust estimator.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 2 lines).

    • create

      public static Point2DRobustEstimator create(Point2DRobustEstimatorListener listener, List<Line2D> lines, double[] qualityScores)
      Creates a 2D point robust estimator based on 2D line samples and using provided listener and lines and default estimator method.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a 2D point.
      qualityScores - quality scores corresponding to each provided line.
      Returns:
      an instance of a 2D point robust estimator.
      Throws:
      IllegalArgumentException - if provided list of lines don't have the same size as the list of provided quality scores, or if their size is not greater or equal than MINIMUM_SIZE.

    • estimate

      public abstract Point2D estimate() throws LockedException, NotReadyException, com.irurueta.numerical.robust.RobustEstimatorException
      Estimates a 2D point using a robust estimator and the best set of 2D lines that intersect into the estimated 2D point.
      Returns:
      a 2D point.
      Throws:
      LockedException - if robust estimator is locked because an estimation is already in progress.
      NotReadyException - if provided input data is not enough to start the estimation.
      com.irurueta.numerical.robust.RobustEstimatorException - if estimation fails for any reason (i.e. numerical instability, no solution available, etc).

    • getMethod

      public abstract com.irurueta.numerical.robust.RobustEstimatorMethod getMethod()
      Returns method being used for robust estimation.
      Returns:
      method being used for robust estimation.

    • residual

      protected double residual(Point2D p, Line2D line)
      Computes the residual between a 2D point and a line.
      Parameters:
      p - a 2D point.
      line - a 2D line.
      Returns:
      residual.

    • attemptRefine

      protected Point2D attemptRefine(Point2D point)
      Attempts to refine provided solution if refinement is requested. This method returns a refined solution or the same provided solution if refinement is not requested or has failed. If refinement is enabled, and it is requested to keep covariance, this method will also keep covariance of refined point.
      Parameters:
      point - point estimated by a robust estimator without refinement.
      Returns:
      solution after refinement (if requested) or the provided non-refined solution if not requested or if refinement failed.

    • getRefinementStandardDeviation

      protected abstract double getRefinementStandardDeviation()
      Gets standard deviation used for Levenberg-Marquardt fitting during refinement. Returned value gives an indication of how much variance each residual has. Typically, this value is related to the threshold used on each robust estimation, since residuals of found inliers are within the range of such threshold.
      Returns:
      standard deviation used for refinement.

    • internalSetLines

      private void internalSetLines(List<Line2D> lines)
      Internal method to set list of 2D lines to be used to estimate a 2D point. This method does not check whether estimator is locked or not
      Parameters:
      lines - list of lines to be used to estimate a 2D point
      Throws:
      IllegalArgumentException - if provided list of lines doesn't have a size greater or equal than MINIMUM_SIZE.