code reorganization
Luca
parent
9bc0ddd4a2
commit
2e3dcd2ab7
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@ -100,9 +100,6 @@ gtGraph = imuSimulator.covarianceAnalysisCreateFactorGraph( ...
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%gtGraph.print(sprintf('\nGround Truth Factor graph:\n'));
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%gtValues.print(sprintf('\nGround Truth Values:\n '));
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warning('Additional prior on zerobias')
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warning('Additional PriorFactorLieVector on velocities')
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figure(1)
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hold on;
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plot3DPoints(gtValues);
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@ -150,9 +147,9 @@ for k=1:numMonteCarloRuns
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marginals = Marginals(graph, estimate);
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% for each pose in the trajectory
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for i=1:size(gtMeasurements.deltaMatrix,1)+1
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for i=0:options.trajectoryLength
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% compute estimation errors
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currentPoseKey = symbol('x', i-1);
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currentPoseKey = symbol('x', i);
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gtPosition = gtValues.at(currentPoseKey).translation.vector;
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estPosition = estimate.at(currentPoseKey).translation.vector;
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estR = estimate.at(currentPoseKey).rotation.matrix;
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@ -163,7 +160,7 @@ for k=1:numMonteCarloRuns
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covPosition = estR * cov(4:6,4:6) * estR';
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% compute NEES using (estimationError = estimatedValues - gtValues) and estimated covariances
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NEES(k,i) = errPosition' * inv(covPosition) * errPosition; % distributed according to a Chi square with n = 3 dof
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NEES(k,i+1) = errPosition' * inv(covPosition) * errPosition; % distributed according to a Chi square with n = 3 dof
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end
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figure(2)
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@ -215,7 +212,7 @@ title('NEES normalized by dof VS bounds');
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saveas(gcf,horzcat(folderName,'ANEES-',testName,'.fig'),'fig');
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logFile = horzcat(folderName,'log-',testName);
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save(logFile)
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%save(logFile)
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%% NEES COMPUTATION (Bar-Shalom 2001, Section 5.4)
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% the nees for a single experiment (i) is defined as
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@ -11,7 +11,7 @@ import gtsam.*;
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graph = NonlinearFactorGraph;
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% Iterate through the trajectory
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for i=0:size(measurements.deltaMatrix, 1);
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for i=0:length(measurements)
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% Get the current pose
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currentPoseKey = symbol('x', i);
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currentPose = values.at(currentPoseKey);
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@ -41,12 +41,12 @@ for i=0:size(measurements.deltaMatrix, 1);
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end
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else
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prevPoseKey = symbol('x', i-1);
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%% Add Between factors
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if options.includeBetweenFactors == 1
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prevPoseKey = symbol('x', i-1);
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% Create the noisy pose estimate
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deltaPose = Pose3.Expmap(measurements.deltaMatrix(i,:)' ...
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deltaPose = Pose3.Expmap(measurements(i).deltaVector ...
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+ (measurementNoise.poseNoiseVector .* randn(6,1))); % added noise
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% Add the between factor to the graph
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graph.add(BetweenFactorPose3(prevPoseKey, currentPoseKey, deltaPose, noiseModels.noisePose));
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@ -57,50 +57,53 @@ for i=0:size(measurements.deltaMatrix, 1);
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% Update keys
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currentVelKey = symbol('v', i); % not used if includeIMUFactors is false
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currentBiasKey = symbol('b', i); % not used if includeIMUFactors is false
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% Generate IMU measurements with noise
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imuAccel = measurements.imu.accel(i,:)' ...
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+ (measurementNoise.imu.accelNoiseVector .* randn(3,1)); % added noise
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imuGyro = measurements.imu.gyro(i,:)' ...
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+ (measurementNoise.imu.gyroNoiseVector .* randn(3,1)); % added noise
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if options.imuFactorType == 2
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% Initialize preintegration
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imuMeasurement = gtsam.CombinedImuFactorPreintegratedMeasurements(...
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metadata.imu.zeroBias, ...
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metadata.imu.AccelerometerSigma.^2 * eye(3), ...
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metadata.imu.GyroscopeSigma.^2 * eye(3), ...
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metadata.imu.IntegrationSigma.^2 * eye(3), ...
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metadata.imu.BiasAccelerometerSigma.^2 * eye(3), ...
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metadata.imu.BiasGyroscopeSigma.^2 * eye(3), ...
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metadata.imu.BiasAccOmegaInit.^2 * eye(6));
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% Preintegrate
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imuMeasurement.integrateMeasurement(imuAccel, imuGyro, measurements.imu.deltaT(i));
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% Add Imu factor
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graph.add(CombinedImuFactor( ...
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currentPoseKey-1, currentVelKey-1, ...
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currentPoseKey, currentVelKey, ...
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currentBiasKey-1, currentBiasKey, ...
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imuMeasurement, ...
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metadata.imu.g, metadata.imu.omegaCoriolis, ...
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noiseModel.Isotropic.Sigma(15, metadata.imu.epsBias)));
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else % IMU type 1
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if options.imuFactorType == 1
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% Initialize preintegration
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imuMeasurement = gtsam.ImuFactorPreintegratedMeasurements(...
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metadata.imu.zeroBias, ...
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metadata.imu.AccelerometerSigma.^2 * eye(3), ...
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metadata.imu.GyroscopeSigma.^2 * eye(3), ...
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metadata.imu.IntegrationSigma.^2 * eye(3));
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% Generate IMU measurements with noise
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for j=1:length(measurements(i).imu) % all measurements to preintegrate
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imuAccel = measurements(i).imu(j).accel ...
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+ (measurementNoise.imu.accelNoiseVector .* randn(3,1)); % added noise
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imuGyro = measurements(i).imu(j).gyro ...
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+ (measurementNoise.imu.gyroNoiseVector .* randn(3,1)); % added noise
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% Preintegrate
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imuMeasurement.integrateMeasurement(imuAccel, imuGyro, measurements.imu.deltaT(i));
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imuMeasurement.integrateMeasurement(imuAccel, imuGyro, measurements(i).imu(j).deltaT);
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% Add Imu factor
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graph.add(ImuFactor(currentPoseKey-1, currentVelKey-1, currentPoseKey, currentVelKey, ...
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currentBiasKey-1, imuMeasurement, metadata.imu.g, metadata.imu.omegaCoriolis));
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% Add between factor on biases
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graph.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, metadata.imu.zeroBias, ...
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noiseModel.Isotropic.Sigma(6, metadata.imu.epsBias)));
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% Additional prior on zerobias
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%graph.add(PriorFactorConstantBias(currentBiasKey, metadata.imu.zeroBias, ...
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% noiseModel.Isotropic.Sigma(6, metadata.imu.epsBias)));
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end
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end
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if options.imuFactorType == 2
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% % Initialize preintegration
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% imuMeasurement = gtsam.CombinedImuFactorPreintegratedMeasurements(...
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% metadata.imu.zeroBias, ...
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% metadata.imu.AccelerometerSigma.^2 * eye(3), ...
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% metadata.imu.GyroscopeSigma.^2 * eye(3), ...
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% metadata.imu.IntegrationSigma.^2 * eye(3), ...
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% metadata.imu.BiasAccelerometerSigma.^2 * eye(3), ...
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% metadata.imu.BiasGyroscopeSigma.^2 * eye(3), ...
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% metadata.imu.BiasAccOmegaInit.^2 * eye(6));
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% % Preintegrate
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% imuMeasurement.integrateMeasurement(imuAccel, imuGyro, measurements.imu.deltaT(i));
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% % Add Imu factor
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% graph.add(CombinedImuFactor( ...
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% currentPoseKey-1, currentVelKey-1, ...
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% currentPoseKey, currentVelKey, ...
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% currentBiasKey-1, currentBiasKey, ...
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% imuMeasurement, ...
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% metadata.imu.g, metadata.imu.omegaCoriolis, ...
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% noiseModel.Isotropic.Sigma(15, metadata.imu.epsBias)));
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end
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end % end of IMU factor creation
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@ -115,7 +118,7 @@ for i=0:size(measurements.deltaMatrix, 1);
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landmarkKey = symbol('p', j);
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try
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Z = gtCamera.project(gtLandmarkPoints(j));
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%% TO-DO probably want to do some type of filtering on the measurement values, because
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% TO-DO probably want to do some type of filtering on the measurement values, because
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% they might not all be valid
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graph.add(GenericProjectionFactorCal3_S2(Z, cameraMeasurementNoise, currentPoseKey, landmarkKey, K));
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catch
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@ -9,8 +9,8 @@ import gtsam.*;
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values = Values;
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warning('fake angles! TODO: use constructor from roll-pitch-yaw when using real data')
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warning('using identity rotation')
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warning('fake angles! TODO: use constructor from roll-pitch-yaw when using real data')
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warning('using identity rotation')
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if options.useRealData == 1
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%% Create a ground truth trajectory from Real data (if available)
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@ -18,47 +18,69 @@ if options.useRealData == 1
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gtScenario = load('truth_scen2.mat', 'Time', 'Lat', 'Lon', 'Alt', 'Roll', 'Pitch', 'Heading',...
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'VEast', 'VNorth', 'VUp');
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Org_lat = gtScenario.Lat(1);
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Org_lon = gtScenario.Lon(1);
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initialPositionECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(1); gtScenario.Lon(1); gtScenario.Alt(1)]);
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% Limit the trajectory length
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options.trajectoryLength = min([length(gtScenario.Lat) options.trajectoryLength+1]);
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options.trajectoryLength = min([length(gtScenario.Lat) options.trajectoryLength]);
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fprintf('Scenario Ind: ');
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for i=1:options.trajectoryLength+1
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% Update the pose key
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currentPoseKey = symbol('x', i-1);
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% Generate the current pose
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scenarioInd = options.subsampleStep * (i-1) + 1;
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for i=0:options.trajectoryLength
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scenarioInd = options.subsampleStep * i + 1;
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fprintf('%d, ', scenarioInd);
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if mod(i,20) == 0
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fprintf('\n');
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end
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gtECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(scenarioInd); gtScenario.Lon(scenarioInd); gtScenario.Alt(scenarioInd)]);
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% truth in ENU
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dX = gtECEF(1) - initialPositionECEF(1);
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dY = gtECEF(2) - initialPositionECEF(2);
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dZ = gtECEF(3) - initialPositionECEF(3);
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[xlt, ylt, zlt] = imuSimulator.ct2ENU(dX, dY, dZ,Org_lat, Org_lon);
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if (mod(i,20) == 0) fprintf('\n'); end
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gtPosition = [xlt, ylt, zlt]';
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gtRotation = Rot3; %Rot3.ypr(gtScenario.Heading(scenarioInd), gtScenario.Pitch(scenarioInd), gtScenario.Roll(scenarioInd));
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currentPose = Pose3(gtRotation, Point3(gtPosition));
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% Add values
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%% Generate the current pose
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currentPoseKey = symbol('x', i);
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currentPose = imuSimulator.getPoseFromGtScenario(gtScenario,scenarioInd);
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% add to values
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values.insert(currentPoseKey, currentPose);
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% Generate the measurement. The first pose is considered the prior, so
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% it has no measurement
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if i > 1
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%% gt Between measurements
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if options.includeBetweenFactors == 1 && i > 0
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prevPose = values.at(currentPoseKey - 1);
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deltaPose = prevPose.between(currentPose);
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measurements.deltaMatrix(i-1,:) = Pose3.Logmap(deltaPose);
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measurements(i).deltaVector = Pose3.Logmap(deltaPose);
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end
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%% gt IMU measurements
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if options.includeIMUFactors == 1
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currentVelKey = symbol('v', i);
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currentBiasKey = symbol('b', i);
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deltaT = 0.01; % amount of time between IMU measurements
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if i == 0
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currentVel = [0 0 0]';
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else
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% integrate & store intermediate measurements
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for j=1:options.subsampleStep % we integrate all the intermediate measurements
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scenarioIndIMU1 = scenarioInd+j-1;
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scenarioIndIMU2 = scenarioInd+j;
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IMUPose1 = imuSimulator.getPoseFromGtScenario(gtScenario,scenarioIndIMU1);
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IMUPose2 = imuSimulator.getPoseFromGtScenario(gtScenario,scenarioIndIMU2);
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IMUdeltaPose = IMUPose1.between(IMUPose2);
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IMUdeltaPoseVector = Pose3.Logmap(IMUdeltaPose);
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IMUdeltaRotVector = IMUdeltaPoseVector(1:3);
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IMUdeltaPositionVector = IMUPose2.translation.vector - IMUPose1.translation.vector; % translation in absolute frame
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measurements(i).imu(j).deltaT = deltaT;
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% gyro rate: Logmap(R_i' * R_i+1) / deltaT
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measurements(i).imu(j).gyro = IMUdeltaRotVector./deltaT;
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% acc = (deltaPosition - initialVel * dT) * (2/dt^2)
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measurements(i).imu(j).accel = (IMUdeltaPositionVector - currentVel.*deltaT).*(2/(deltaT*deltaT));
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% Update velocity
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currentVel = IMUdeltaPositionVector./deltaT;
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end
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end
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% Add Values: velocity and bias
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values.insert(currentVelKey, LieVector(currentVel));
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values.insert(currentBiasKey, metadata.imu.zeroBias);
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end
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end
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fprintf('\n');
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else
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error('Please use RealData')
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%% Create a random trajectory as ground truth
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currentPose = Pose3; % initial pose % initial pose
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@ -87,41 +109,5 @@ else
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end % end of random trajectory creation
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end % end of else
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%% Create IMU measurements and Values for the trajectory
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if options.includeIMUFactors == 1
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currentVel = [0 0 0]; % initial velocity (used to generate IMU measurements)
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deltaT = 0.1; % amount of time between IMU measurements
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% Iterate over the deltaMatrix to generate appropriate IMU measurements
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for i = 0:size(measurements.deltaMatrix, 1)
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% Update Keys
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currentVelKey = symbol('v', i);
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currentBiasKey = symbol('b', i);
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if i == 0
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% Add initial values
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currentVel = [0 0 0];
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values.insert(currentVelKey, LieVector(currentVel'));
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values.insert(currentBiasKey, metadata.imu.zeroBias);
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else
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measurements.imu.deltaT(i) = deltaT;
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% create accel and gyro measurements based on
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measurements.imu.gyro(i,:) = measurements.deltaMatrix(i, 1:3)./measurements.imu.deltaT(i);
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% acc = (deltaPosition - initialVel * dT) * (2/dt^2)
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measurements.imu.accel(i,:) = (measurements.deltaMatrix(i, 4:6) ...
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- currentVel.*measurements.imu.deltaT(i)).*(2/(measurements.imu.deltaT(i)*measurements.imu.deltaT(i)));
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% Update velocity
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currentVel = measurements.deltaMatrix(i,4:6)./measurements.imu.deltaT(i);
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% Add Values: velocity and bias
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values.insert(currentVelKey, LieVector(currentVel'));
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values.insert(currentBiasKey, metadata.imu.zeroBias);
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end
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end
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end % end of IMU measurements
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end % end of function
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@ -0,0 +1,25 @@
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function currentPose = getPoseFromGtScenario(gtScenario,scenarioInd)
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% gtScenario contains vectors (Lat, Lon, Alt, Roll, Pitch, Yaw)
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% The function picks the index 'scenarioInd' in those vectors and
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% computes the corresponding pose by
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% 1) Converting (Lat,Lon,Alt) to local coordinates
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% 2) Converting (Roll,Pitch,Yaw) to a rotation matrix
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import gtsam.*;
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Org_lat = gtScenario.Lat(1);
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Org_lon = gtScenario.Lon(1);
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initialPositionECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(1); gtScenario.Lon(1); gtScenario.Alt(1)]);
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gtECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(scenarioInd); gtScenario.Lon(scenarioInd); gtScenario.Alt(scenarioInd)]);
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% truth in ENU
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dX = gtECEF(1) - initialPositionECEF(1);
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dY = gtECEF(2) - initialPositionECEF(2);
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dZ = gtECEF(3) - initialPositionECEF(3);
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[xlt, ylt, zlt] = imuSimulator.ct2ENU(dX, dY, dZ,Org_lat, Org_lon);
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gtPosition = [xlt, ylt, zlt]';
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gtRotation = Rot3; %Rot3.ypr(gtScenario.Heading(scenarioInd), gtScenario.Pitch(scenarioInd), gtScenario.Roll(scenarioInd));
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currentPose = Pose3(gtRotation, Point3(gtPosition));
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end
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