143 lines
6.4 KiB
Matlab
143 lines
6.4 KiB
Matlab
import gtsam.*;
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disp('Example of application of ISAM2 for GPS-aided navigation on the KITTI VISION BENCHMARK SUITE (http://www.computervisiononline.com/dataset/kitti-vision-benchmark-suite)')
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%% Read metadata and compute relative sensor pose transforms
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% IMU metadata
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disp('-- Reading sensor metadata')
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IMU_metadata = importdata(findExampleDataFile('KittiEquivBiasedImu_metadata.txt'));
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IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2);
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IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz;
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IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]);
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if ~IMUinBody.equals(Pose3, 1e-5)
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error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same';
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end
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%% Read data
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disp('-- Reading sensor data from file')
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% IMU data
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IMU_data = importdata(findExampleDataFile('KittiEquivBiasedImu.txt'));
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IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2);
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imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false);
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[IMU_data.acc_omega] = deal(imum{:});
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clear imum
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% GPS data
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GPS_data = importdata(findExampleDataFile('KittiGps_converted.txt'));
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GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2);
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for i = 1:numel(GPS_data)
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GPS_data(i).Position = gtsam.Point3(GPS_data(i).X, GPS_data(i).Y, GPS_data(i).Z);
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end
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noiseModelGPS = noiseModel.Diagonal.Precisions([ [0;0;0]; 1.0/0.07 * [1;1;1] ]);
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firstGPSPose = 2;
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GPSskip = 10; % Skip this many GPS measurements each time
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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 = [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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sigma_init_x = noiseModel.Isotropic.Precisions([ 0.0; 0.0; 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.Sigmas([ 0.100; 0.100; 0.100; 5.00e-05; 5.00e-05; 5.00e-05 ]);
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sigma_between_b = [ IMU_metadata.AccelerometerBiasSigma * Matrix::Ones(3,1); IMU_metadata.GyroscopeBiasSigma * Matrix::Ones(3,1) ];
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g = [0;0;-9.8];
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w_coriolis = [0;0;0];
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%% Solver object
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isamParams = ISAM2Params;
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isamParams.setFactorization('CHOLESKY');
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isamParams.setRelinearizeSkip(10);
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isam = gtsam.ISAM2(isamParams);
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newFactors = NonlinearFactorGraph;
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newValues = Values;
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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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IMUtimes = [IMU_data.Time];
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disp('-- Starting main loop: inference is performed at each time step, but we plot trajectory every 10 steps')
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for measurementIndex = 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',measurementIndex);
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currentVelKey = symbol('v',measurementIndex);
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currentBiasKey = symbol('b',measurementIndex);
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t = GPS_data(measurementIndex, 1).Time;
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if measurementIndex == firstGPSPose
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%% Create initial estimate and prior on initial pose, velocity, and biases
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newValues.insert(currentPoseKey, currentPoseGlobal);
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newValues.insert(currentVelKey, currentVelocityGlobal);
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newValues.insert(currentBiasKey, currentBias);
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newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x));
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newFactors.add(PriorFactorVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
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newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b));
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else
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t_previous = GPS_data(measurementIndex-1, 1).Time;
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%% Summarize IMU data between the previous GPS measurement and now
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IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t);
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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, g, w_coriolis));
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% Bias evolution as given in the IMU metadata
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newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), ...
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noiseModel.Diagonal.Sigmas(sqrt(numel(IMUindices)) * sigma_between_b)));
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% Create GPS factor
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GPSPose = Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position);
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if mod(measurementIndex, GPSskip) == 0
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newFactors.add(PriorFactorPose3(currentPoseKey, GPSPose, noiseModelGPS));
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end
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% Add initial value
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newValues.insert(currentPoseKey, GPSPose);
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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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% We accumulate 2*GPSskip GPS measurements before updating the solver at
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% first so that the heading becomes observable.
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if measurementIndex > firstGPSPose + 2*GPSskip
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isam.update(newFactors, newValues);
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newFactors = NonlinearFactorGraph;
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newValues = Values;
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if rem(measurementIndex,10)==0 % plot every 10 time steps
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cla;
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plot3DTrajectory(isam.calculateEstimate, 'g-');
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title('Estimated trajectory using ISAM2 (IMU+GPS)')
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xlabel('[m]')
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ylabel('[m]')
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zlabel('[m]')
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axis equal
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drawnow;
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end
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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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end % end main loop
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disp('-- Reached end of sensor data')
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