SLAM.LoopClosure.RGBDKeyframes.ValidFrame

Parameters, inputs, outputs, defaults and equations from the source file.

function ValidFrame
    input Frame frame;
    output Boolean valid;
  protected
    Integer measured;
    Real mass;
    Real norm;
    Real mean;
    Real inverseCovariance[poseDimension,poseDimension];
    Real scaledPivot;
    Boolean covarianceValid;
  algorithm
    valid := frame.generation >= 1 and frame.generation <= identifierLimit
      and frame.id >= 1 and frame.id < identifierLimit
      and frame.epoch >= 0 and frame.epoch <= identifierLimit
      and frame.imageTime >= 0 and frame.imageTime <= 1e9
      and frame.count >= 0 and frame.count <= featureCapacity
      and frame.vocabularyVersion >= 1 and frame.vocabularyVersion <= identifierLimit
      and ValidCalibration(frame.rgbCalibration,frame.rgbSize)
      and ValidCalibration(frame.depthCalibration,frame.depthSize)
      and frame.disparityNoise > 0 and frame.disparityNoise <= 100
      and frame.noiseReferenceFx > 0 and frame.noiseReferenceFx <= 1e6
      and frame.baseline > 0 and frame.baseline <= 10
      and RGBDUncertaintyProper(frame.opticalToBody)
      and RGBDUncertaintyProper(frame.bodyRotation);
    for axis in 1:dimension loop
      valid := valid and abs(frame.bodyPosition[axis]) <= 1e6
        and abs(frame.cameraOriginBody[axis]) <= 1e6;
    end for;
    (inverseCovariance,covarianceValid,scaledPivot) :=
      RGBDUncertaintyInverse6(frame.poseCovariance,1e-10);
    valid := valid and covarianceValid;
    measured := 0;
    for feature in 1:featureCapacity loop
      if frame.enabled[feature] then
        measured := measured+1;
        valid := valid and feature <= frame.count
          and frame.pixels[feature,1] >= 0 and frame.pixels[feature,1] < frame.rgbSize[2]
          and frame.pixels[feature,2] >= 0 and frame.pixels[feature,2] < frame.rgbSize[1]
          and frame.opticalPoint[feature,3] > 0;
        for axis in 1:dimension loop
          valid := valid and abs(frame.opticalPoint[feature,axis]) <= 1e6;
        end for;
        norm := 0.0;
        mean := 0.0;
        for sample in 1:descriptorSize loop
          valid := valid and abs(frame.descriptor[feature,sample]) <= 1.000001;
          norm := norm+frame.descriptor[feature,sample]^2;
          mean := mean+frame.descriptor[feature,sample];
        end for;
        valid := valid and abs(norm-1.0) <= 1e-6 and abs(mean) <= 1e-6;
      end if;
    end for;
    valid := valid and measured >= 8;
    mass := 0.0;
    for word in 1:wordCapacity loop
      valid := valid and frame.histogram[word] >= 0 and frame.histogram[word] <= 1;
      mass := mass+frame.histogram[word];
    end for;
    valid := valid and abs(mass-1.0) <= 1e-6;
  end ValidFrame;