127 lines
5.3 KiB
Matlab
127 lines
5.3 KiB
Matlab
%close all
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%clc
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import gtsam.*;
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%% Read data
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IMU_metadata = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu_metadata.txt'));
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IMU_data = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu.txt'));
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% Make text file column headers into struct fields
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IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2);
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IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2);
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GPS_metadata = importdata(gtsam.findExampleDataFile('KittiGps_metadata.txt'));
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GPS_data = importdata(gtsam.findExampleDataFile('KittiGps.txt'));
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% Make text file column headers into struct fields
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GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2);
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GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2);
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%% Convert GPS from lat/long to meters
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[ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] );
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for i = 1:numel(x)
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GPS_data(i).Position = gtsam.Point3(x(i), y(i), GPS_data(i).Altitude);
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end
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% % Calculate GPS sigma in meters
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% [ xSig, ySig, ~ ] = deg2utm( [GPS_data.Latitude] + [GPS_data.PositionSigma], ...
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% [GPS_data.Longitude] + [GPS_data.PositionSigma]);
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% xSig = xSig - x;
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% ySig = ySig - y;
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%% Start at time of first GPS measurement
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firstGPSPose = 2;
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%% Get initial conditions for the estimated trajectory
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currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame)
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currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning
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currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
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%% Solver object
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isamParams = ISAM2Params;
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isamParams.setFactorization('QR');
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isamParams.setRelinearizeSkip(1);
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isam = gtsam.ISAM2(isamParams);
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newFactors = NonlinearFactorGraph;
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newValues = Values;
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%% Create initial estimate and prior on initial pose, velocity, and biases
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newValues.insert(symbol('x',firstGPSPose), currentPoseGlobal);
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newValues.insert(symbol('v',firstGPSPose), currentVelocityGlobal);
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newValues.insert(symbol('b',1), currentBias);
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sigma_init_x = noiseModel.Diagonal.Precisions([0;0;0; 1;1;1]);
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sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
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sigma_init_b = noiseModel.Isotropic.Sigma(6, 100000.0);
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newFactors.add(PriorFactorPose3(symbol('x',firstGPSPose), currentPoseGlobal, sigma_init_x));
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newFactors.add(PriorFactorLieVector(symbol('v',firstGPSPose), currentVelocityGlobal, sigma_init_v));
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newFactors.add(PriorFactorConstantBias(symbol('b',1), currentBias, sigma_init_b));
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%% Main loop:
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% (1) we read the measurements
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% (2) we create the corresponding factors in the graph
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% (3) we solve the graph to obtain and optimal estimate of robot trajectory
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for poseIndex = firstGPSPose:length(GPS_data)
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% At each non=IMU measurement we initialize a new node in the graph
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currentPoseKey = symbol('x',poseIndex);
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currentVelKey = symbol('v',poseIndex);
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currentBiasKey = symbol('b',1);
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if poseIndex > firstGPSPose
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% Summarize IMU data between the previous GPS measurement and now
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IMUindices = find([IMU_data.Time] > GPS_data(poseIndex-1).Time ...
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& [IMU_data.Time] <= GPS_data(poseIndex).Time);
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currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ...
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currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ...
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IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3));
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for imuIndex = IMUindices
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accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ];
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omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ];
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deltaT = IMU_data(imuIndex).dt;
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currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT);
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end
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% Create IMU factor
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newFactors.add(ImuFactor( ...
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currentPoseKey-1, currentVelKey-1, ...
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currentPoseKey, currentVelKey, ...
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currentBiasKey, currentSummarizedMeasurement, [0;0;-9.8], [0;0;0]));
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% Create GPS factor
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newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position), ...
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noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(poseIndex).PositionSigma).^2*ones(3,1) ])));
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% Add initial value
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newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position));
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newValues.insert(currentVelKey, currentVelocityGlobal);
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%newValues.insert(currentBiasKey, currentBias);
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% Update solver
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% =======================================================================
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isam.update(newFactors, newValues);
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newFactors = NonlinearFactorGraph;
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newValues = Values;
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cla;
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plot3DTrajectory(isam.calculateEstimate, 'g-');
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drawnow;
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% =======================================================================
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currentPoseGlobal = isam.calculateEstimate(currentPoseKey);
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currentVelocityGlobal = isam.calculateEstimate(currentVelKey);
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currentBias = isam.calculateEstimate(currentBiasKey);
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end
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end
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disp('TODO: display results')
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% figure(1)
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% hold on;
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% plot(positions(1,:), positions(2,:), '-b');
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% plot3DTrajectory(isam.calculateEstimate, 'g-');
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% axis equal;
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% legend('true trajectory', 'traj integrated in body', 'traj integrated in nav')
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