restructuring code to utilize functions and reduce size of primary script
djensen3
parent
8367d45e48
commit
b85ebb501d
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@ -10,15 +10,12 @@ clear all
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close all
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%% Configuration
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useRealData = 0; % controls whether or not to use the Real data (is available) as the ground truth traj
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%options.
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includeBetweenFactors = 1; % if true, BetweenFactors will be generated between consecutive poses
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includeIMUFactors = 0; % if true, IMU type 1 Factors will be generated for the random trajectory
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includeCameraFactors = 0; % not fully implemented yet
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trajectoryLength = 4; % length of the ground truth trajectory
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subsampleStep = 20;
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options.useRealData = 0; % controls whether or not to use the real data (if available) as the ground truth traj
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options.includeBetweenFactors = 1; % if true, BetweenFactors will be generated between consecutive poses
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options.includeIMUFactors = 0; % if true, IMU type 1 Factors will be generated for the trajectory
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options.includeCameraFactors = 0; % not fully implemented yet
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options.trajectoryLength = 4; % length of the ground truth trajectory
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options.subsampleStep = 20;
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numMonteCarloRuns = 2;
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@ -29,26 +26,29 @@ cameraMeasurementNoiseSigma = 1.0;
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cameraMeasurementNoise = noiseModel.Isotropic.Sigma(2,cameraMeasurementNoiseSigma);
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% Create landmarks
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if includeCameraFactors == 1
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if options.includeCameraFactors == 1
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for i = 1:numberOfLandmarks
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gtLandmarkPoints(i) = Point3( ...
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[rand()*20*(trajectoryLength*1.2) + 15*20; ... % uniformly distributed in the x axis along 120% of the trajectory length, starting after 15 poses
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... % uniformly distributed in the x axis along 120% of the trajectory length, starting after 15 poses
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[rand()*20*(options.trajectoryLength*1.2) + 15*20; ...
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randn()*20; ... % normally distributed in the y axis with a sigma of 20
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randn()*20]); % normally distributed in the z axis with a sigma of 20
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end
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end
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%% Imu metadata
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epsBias = 1e-10; % was 1e-7
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zeroBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
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IMU_metadata.AccelerometerSigma = 1e-5;
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IMU_metadata.GyroscopeSigma = 1e-7;
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IMU_metadata.IntegrationSigma = 1e-4;
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IMU_metadata.BiasAccelerometerSigma = epsBias;
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IMU_metadata.BiasGyroscopeSigma = epsBias;
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IMU_metadata.BiasAccOmegaInit = epsBias;
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metadata.imu.epsBias = 1e-10; % was 1e-7
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metadata.imu.zeroBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
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metadata.imu.AccelerometerSigma = 1e-5;
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metadata.imu.GyroscopeSigma = 1e-7;
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metadata.imu.IntegrationSigma = 1e-4;
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metadata.imu.BiasAccelerometerSigma = metadata.imu.epsBias;
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metadata.imu.BiasGyroscopeSigma = metadata.imu.epsBias;
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metadata.imu.BiasAccOmegaInit = metadata.imu.epsBias;
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metadata.imu.g = [0;0;0];
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metadata.imu.omegaCoriolis = [0;0;0];
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noiseVel = noiseModel.Isotropic.Sigma(3, 1e-2); % was 0.1
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noiseBias = noiseModel.Isotropic.Sigma(6, epsBias);
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noiseBias = noiseModel.Isotropic.Sigma(6, metadata.imu.epsBias);
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noisePriorBias = noiseModel.Isotropic.Sigma(6, 1e-4);
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%% Between metadata
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@ -61,170 +61,32 @@ noisePose = noiseModel.Diagonal.Sigmas(noiseVectorPose);
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testName = sprintf('sa-%1.2g-sc-%1.2g',sigma_ang,sigma_cart)
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folderName = 'results/'
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%% Create ground truth trajectory
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gtValues = Values;
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%% Create ground truth trajectory and measurements
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[gtValues, gtMeasurements] = imuSimulator.covarianceAnalysisCreateTrajectory(options, metadata);
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if useRealData == 1
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%% Create a ground truth trajectory from Real data (if available)
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fprintf('\nUsing real data as ground truth\n');
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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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trajectoryLength = min([length(gtScenario.Lat) trajectoryLength]);
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for i=1:trajectoryLength
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currentPoseKey = symbol('x', i-1);
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scenarioInd = subsampleStep * (i-1) + 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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% Add values
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gtValues.insert(currentPoseKey, currentPose);
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end
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else
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%% Create a random trajectory as ground truth
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currentVel = [0; 0; 0]; % initial velocity (used to generate IMU measurements)
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currentPose = Pose3; % initial pose % initial pose
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deltaT = 0.1; % amount of time between IMU measurements
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g = [0; 0; 0]; % gravity
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omegaCoriolis = [0; 0; 0]; % Coriolis
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unsmooth_DP = 0.5; % controls smoothness on translation norm
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unsmooth_DR = 0.1; % controls smoothness on rotation norm
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fprintf('\nCreating a random ground truth trajectory\n');
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currentPoseKey = symbol('x', 0);
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gtValues.insert(currentPoseKey, currentPose);
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for i=1:trajectoryLength
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currentPoseKey = symbol('x', i);
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gtDeltaPosition = unsmooth_DP*randn(3,1) + [20;0;0]; % create random vector with mean = [20 0 0]
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gtDeltaRotation = unsmooth_DR*randn(3,1) + [0;0;0]; % create random rotation with mean [0 0 0]
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measurements.gtDeltaMatrix(i,:) = [gtDeltaRotation; gtDeltaPosition];
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gtMeasurements.deltaPose = Pose3.Expmap(measurements.gtDeltaMatrix(i,:)');
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% "Deduce" ground truth measurements
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% deltaPose are the gt measurements - save them in some structure
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currentPose = currentPose.compose(gtMeasurements.deltaPose);
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gtValues.insert(currentPoseKey, currentPose);
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end
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end
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% we computed gtValues
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%% Create ground truth graph
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% Set up noise models
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gtNoiseModels.noisePose = noisePose;
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gtNoiseModels.noiseVel = noiseVel;
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gtNoiseModels.noiseBias = noiseBias;
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gtNoiseModels.noisePriorPose = noisePose;
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gtNoiseModels.noisePriorBias = noisePriorBias;
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gtGraph = NonlinearFactorGraph;
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for i=0:trajectoryLength
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currentPoseKey = symbol('x', i);
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currentPose = gtValues.at(currentPoseKey);
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% Set measurement noise to 0, because this is ground truth
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gtMeasurementNoise.poseNoiseVector = [0 0 0 0 0 0];
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gtMeasurementNoise.imu.accelNoiseVector = [0 0 0];
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gtMeasurementNoise.imu.gyroNoiseVector = [0 0 0];
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gtMeasurementNoise.cameraPixelNoiseVector = [0 0];
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if i==0
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%% first time step, add priors
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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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gtGraph.add(PriorFactorPose3(currentPoseKey, currentPose, noisePose));
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measurements.posePrior = currentPose;
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if includeIMUFactors == 1
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currentVelKey = symbol('v', 0);
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currentBiasKey = symbol('b', 0);
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gtValues.insert(currentVelKey, LieVector(currentVel));
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gtValues.insert(currentBiasKey, zeroBias);
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gtGraph.add(PriorFactorLieVector(currentVelKey, LieVector(currentVel), noiseVel));
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gtGraph.add(PriorFactorConstantBias(currentBiasKey, zeroBias, noisePriorBias));
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end
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if includeCameraFactors == 1
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pointNoiseSigma = 0.1;
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pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
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gtGraph.add(PriorFactorPoint3(symbol('p',1), gtLandmarkPoints(1), pointPriorNoise));
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end
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else
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%% other factors
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if includeBetweenFactors == 1
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prevPoseKey = symbol('x', i-1);
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prevPose = gtValues.at(prevPoseKey);
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deltaPose = prevPose.between(currentPose);
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measurements.gtDeltaMatrix(i,:) = Pose3.Logmap(deltaPose);
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% Add the factor to the factor graph
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gtGraph.add(BetweenFactorPose3(prevPoseKey, currentPoseKey, deltaPose, noisePose));
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end
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%% Add IMU factors
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if includeIMUFactors == 1
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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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% create accel and gyro measurements based on
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gtMeasurements.imu.gyro = measurements.gtDeltaMatrix(i, 1:3)'./deltaT;
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% acc = (deltaPosition - initialVel * dT) * (2/dt^2)
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gtMeasurements.imu.accel = (measurements.gtDeltaMatrix(i, 4:6)' - currentVel.*deltaT).*(2/(deltaT*deltaT));
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% Initialize preintegration
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imuMeasurement = gtsam.ImuFactorPreintegratedMeasurements(...
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zeroBias, ...
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IMU_metadata.AccelerometerSigma.^2 * eye(3), ...
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IMU_metadata.GyroscopeSigma.^2 * eye(3), ...
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IMU_metadata.IntegrationSigma.^2 * eye(3));
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% Preintegrate
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imuMeasurement.integrateMeasurement(gtMeasurements.imu.accel, gtMeasurements.imu.gyro, deltaT);
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% Add Imu factor
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gtGraph.add(ImuFactor(currentPoseKey-1, currentVelKey-1, currentPoseKey, currentVelKey, ...
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currentBiasKey-1, imuMeasurement, g, omegaCoriolis));
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% Add between on biases
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gtGraph.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, zeroBias, ...
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noiseModel.Isotropic.Sigma(6, epsBias)));
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% Additional prior on zerobias
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gtGraph.add(PriorFactorConstantBias(currentBiasKey, zeroBias, ...
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noiseModel.Isotropic.Sigma(6, epsBias)));
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% update current velocity
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currentVel = measurements.gtDeltaMatrix(i,4:6)'./deltaT;
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gtValues.insert(currentVelKey, LieVector(currentVel));
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gtValues.insert(currentBiasKey, zeroBias);
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end % end of IMU factor creation
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%% Add Camera factors
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if includeCameraFactors == 1
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% Create camera with the current pose and calibration K (specified above)
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gtCamera = SimpleCamera(currentPose, K);
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% Project landmarks into the camera
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numSkipped = 0;
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for j = 1:length(gtLandmarkPoints)
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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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% they might not all be valid
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gtGraph.add(GenericProjectionFactorCal3_S2(Z, cameraMeasurementNoise, currentPoseKey, landmarkKey, K));
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catch
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% Most likely the point is not within the camera's view, which
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% is fine
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numSkipped = numSkipped + 1;
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end
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end
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%fprintf('(Pose %d) %d landmarks behind the camera\n', i, numSkipped);
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end % end of Camera factor creation
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end % end of else
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end % end of for over trajectory
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[gtGraph, gtValues] = imuSimulator.covarianceAnalysisCreateFactorGraph( ...
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gtMeasurements, ... % ground truth measurements
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gtValues, ... % ground truth Values
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gtNoiseModels, ... % noise models to use in this graph
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gtMeasurementNoise, ... % noise to apply to measurements
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options, ... % options for the graph (e.g. which factors to include)
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metadata); % misc data necessary for factor creation
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%% Display, printing, and plotting of ground truth
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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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@ -239,6 +101,7 @@ axis equal
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disp('Plotted ground truth')
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%% Monte Carlo Runs
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for k=1:numMonteCarloRuns
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fprintf('Monte Carlo Run %d.\n', k');
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% create a new graph
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@ -246,17 +109,17 @@ for k=1:numMonteCarloRuns
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% noisy prior
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currentPoseKey = symbol('x', 0);
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measurements.posePrior = currentPose;
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currentPose = gtValues.at(currentPoseKey);
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gtMeasurements.posePrior = currentPose;
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noisyDelta = noiseVectorPose .* randn(6,1);
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noisyInitialPose = Pose3.Expmap(noisyDelta);
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graph.add(PriorFactorPose3(currentPoseKey, noisyInitialPose, noisePose));
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for i=1:size(measurements.gtDeltaMatrix,1)
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i
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for i=1:size(gtMeasurements.deltaMatrix,1)
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currentPoseKey = symbol('x', i);
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% for each measurement: add noise and add to graph
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noisyDelta = measurements.gtDeltaMatrix(i,:)' + (noiseVectorPose .* randn(6,1));
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noisyDelta = gtMeasurements.deltaMatrix(i,:)' + (noiseVectorPose .* randn(6,1));
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noisyDeltaPose = Pose3.Expmap(noisyDelta);
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% Add the factors to the factor graph
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@ -275,7 +138,7 @@ 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(measurements.gtDeltaMatrix,1)+1
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for i=1:size(gtMeasurements.deltaMatrix,1)+1
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% compute estimation errors
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currentPoseKey = symbol('x', i-1);
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gtPosition = gtValues.at(currentPoseKey).translation.vector;
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@ -0,0 +1,110 @@
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function [ graph, values ] = covarianceAnalysisCreateFactorGraph( measurements, values, noiseModels, measurementNoise, options, metadata)
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% Create a factor graph based on provided measurements, values, and noises.
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% Used for covariance analysis scripts.
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% 'options' contains fields for including various factor types.
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% 'metadata' is a storage variable for miscellaneous factor-specific values
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% Authors: Luca Carlone, David Jensen
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% Date: 2014/04/16
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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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% Get the current pose
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currentPoseKey = symbol('x', i);
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currentPose = values.at(currentPoseKey);
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if i==0
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%% first time step, add priors
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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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graph.add(PriorFactorPose3(currentPoseKey, currentPose, noiseModels.noisePose));
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measurements.posePrior = currentPose;
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if options.includeIMUFactors == 1
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currentVelKey = symbol('v', 0);
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currentBiasKey = symbol('b', 0);
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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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graph.add(PriorFactorLieVector(currentVelKey, LieVector(currentVel), noiseModels.noiseVel));
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graph.add(PriorFactorConstantBias(currentBiasKey, metadata.imu.zeroBias, noiseModels.noisePriorBias));
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end
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if options.includeCameraFactors == 1
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pointNoiseSigma = 0.1;
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pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
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graph.add(PriorFactorPoint3(symbol('p',1), gtLandmarkPoints(1), pointPriorNoise));
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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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% Create the noisy pose estimate
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deltaPose = Pose3.Expmap(measurements.deltaMatrix(i,:)' ...
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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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end % end of Between pose factor creation
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%% Add IMU factors
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if options.includeIMUFactors == 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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% 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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% 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(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 % end of IMU factor creation
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%% Add Camera factors - UNDER CONSTRUCTION !!!! -
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if options.includeCameraFactors == 1
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% Create camera with the current pose and calibration K (specified above)
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gtCamera = SimpleCamera(currentPose, K);
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% Project landmarks into the camera
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numSkipped = 0;
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for j = 1:length(gtLandmarkPoints)
|
||||
landmarkKey = symbol('p', j);
|
||||
try
|
||||
Z = gtCamera.project(gtLandmarkPoints(j));
|
||||
%% TO-DO probably want to do some type of filtering on the measurement values, because
|
||||
% they might not all be valid
|
||||
graph.add(GenericProjectionFactorCal3_S2(Z, cameraMeasurementNoise, currentPoseKey, landmarkKey, K));
|
||||
catch
|
||||
% Most likely the point is not within the camera's view, which
|
||||
% is fine
|
||||
numSkipped = numSkipped + 1;
|
||||
end
|
||||
end
|
||||
%fprintf('(Pose %d) %d landmarks behind the camera\n', i, numSkipped);
|
||||
end % end of Camera factor creation
|
||||
|
||||
end % end of else
|
||||
|
||||
end % end of for over trajectory
|
||||
|
||||
end
|
||||
|
||||
|
|
@ -0,0 +1,112 @@
|
|||
function [ values, measurements] = covarianceAnalysisCreateTrajectory( options, metadata )
|
||||
% Create a trajectory for running covariance analysis scripts.
|
||||
% 'options' contains fields for including various factor types and setting trajectory length
|
||||
% 'metadata' is a storage variable for miscellaneous factor-specific values
|
||||
% Authors: Luca Carlone, David Jensen
|
||||
% Date: 2014/04/16
|
||||
|
||||
import gtsam.*;
|
||||
|
||||
values = Values;
|
||||
|
||||
if options.useRealData == 1
|
||||
%% Create a ground truth trajectory from Real data (if available)
|
||||
fprintf('\nUsing real data as ground truth\n');
|
||||
gtScenario = load('truth_scen2.mat', 'Time', 'Lat', 'Lon', 'Alt', 'Roll', 'Pitch', 'Heading',...
|
||||
'VEast', 'VNorth', 'VUp');
|
||||
|
||||
Org_lat = gtScenario.Lat(1);
|
||||
Org_lon = gtScenario.Lon(1);
|
||||
initialPositionECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(1); gtScenario.Lon(1); gtScenario.Alt(1)]);
|
||||
|
||||
% Limit the trajectory length
|
||||
options.trajectoryLength = min([length(gtScenario.Lat) options.trajectoryLength+1]);
|
||||
|
||||
for i=1:options.trajectoryLength+1
|
||||
% Update the pose key
|
||||
currentPoseKey = symbol('x', i-1);
|
||||
|
||||
% Generate the current pose
|
||||
scenarioInd = options.subsampleStep * (i-1) + 1
|
||||
gtECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario.Lat(scenarioInd); gtScenario.Lon(scenarioInd); gtScenario.Alt(scenarioInd)]);
|
||||
% truth in ENU
|
||||
dX = gtECEF(1) - initialPositionECEF(1);
|
||||
dY = gtECEF(2) - initialPositionECEF(2);
|
||||
dZ = gtECEF(3) - initialPositionECEF(3);
|
||||
[xlt, ylt, zlt] = imuSimulator.ct2ENU(dX, dY, dZ,Org_lat, Org_lon);
|
||||
|
||||
gtPosition = [xlt, ylt, zlt]';
|
||||
gtRotation = Rot3; % Rot3.ypr(gtScenario.Heading(scenarioInd), gtScenario.Pitch(scenarioInd), gtScenario.Roll(scenarioInd));
|
||||
currentPose = Pose3(gtRotation, Point3(gtPosition));
|
||||
|
||||
% Add values
|
||||
values.insert(currentPoseKey, currentPose);
|
||||
|
||||
% Generate the measurement. The first pose is considered the prior, so
|
||||
% it has no measurement
|
||||
if i > 1
|
||||
prevPose = values.at(currentPoseKey - 1);
|
||||
deltaPose = prevPose.between(currentPose);
|
||||
measurements.deltaMatrix(i-1,:) = Pose3.Logmap(deltaPose);
|
||||
end
|
||||
end
|
||||
else
|
||||
%% Create a random trajectory as ground truth
|
||||
currentPose = Pose3; % initial pose % initial pose
|
||||
|
||||
unsmooth_DP = 0.5; % controls smoothness on translation norm
|
||||
unsmooth_DR = 0.1; % controls smoothness on rotation norm
|
||||
|
||||
fprintf('\nCreating a random ground truth trajectory\n');
|
||||
currentPoseKey = symbol('x', 0);
|
||||
values.insert(currentPoseKey, currentPose);
|
||||
|
||||
for i=1:options.trajectoryLength
|
||||
% Update the pose key
|
||||
currentPoseKey = symbol('x', i);
|
||||
|
||||
% Generate the new measurements
|
||||
gtDeltaPosition = unsmooth_DP*randn(3,1) + [20;0;0]; % create random vector with mean = [20 0 0]
|
||||
gtDeltaRotation = unsmooth_DR*randn(3,1) + [0;0;0]; % create random rotation with mean [0 0 0]
|
||||
measurements.deltaMatrix(i,:) = [gtDeltaRotation; gtDeltaPosition];
|
||||
|
||||
% Create the next pose
|
||||
deltaPose = Pose3.Expmap(measurements.deltaMatrix(i,:)');
|
||||
currentPose = currentPose.compose(deltaPose);
|
||||
|
||||
% Add the current pose as a value
|
||||
values.insert(currentPoseKey, currentPose);
|
||||
end % end of random trajectory creation
|
||||
end % end of else
|
||||
|
||||
%% Create IMU measurements and Values for the trajectory
|
||||
if options.includeIMUFactors == 1
|
||||
currentVel = [0 0 0]; % initial velocity (used to generate IMU measurements)
|
||||
deltaT = 0.1; % amount of time between IMU measurements
|
||||
|
||||
% Iterate over the deltaMatrix to generate appropriate IMU measurements
|
||||
for i = 1:size(measurements.deltaMatrix, 1)
|
||||
% Update Keys
|
||||
currentVelKey = symbol('v', i);
|
||||
currentBiasKey = symbol('b', i);
|
||||
|
||||
measurements.imu.deltaT(i) = deltaT;
|
||||
|
||||
% create accel and gyro measurements based on
|
||||
measurements.imu.gyro(i,:) = measurements.deltaMatrix(i, 1:3)./measurements.imu.deltaT(i);
|
||||
|
||||
% acc = (deltaPosition - initialVel * dT) * (2/dt^2)
|
||||
measurements.imu.accel(i,:) = (measurements.deltaMatrix(i, 4:6) ...
|
||||
- currentVel.*measurements.imu.deltaT(i)).*(2/(measurements.imu.deltaT(i)*measurements.imu.deltaT(i)));
|
||||
|
||||
% Update velocity
|
||||
currentVel = measurements.deltaMatrix(i,4:6)./measurements.imu.deltaT(i);
|
||||
|
||||
% Add Values: velocity and bias
|
||||
values.insert(currentVelKey, LieVector(currentVel'));
|
||||
values.insert(currentBiasKey, metadata.imu.zeroBias);
|
||||
end
|
||||
end % end of IMU measurements
|
||||
|
||||
end
|
||||
|
||||
Loading…
Reference in New Issue