153 lines
6.9 KiB
Matlab
153 lines
6.9 KiB
Matlab
close all
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clc
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import gtsam.*;
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disp('Example of application of ISAM2 for visual-inertial 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('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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% VO metadata
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VO_metadata = importdata('KittiRelativePose_metadata.txt');
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VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2);
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VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz;
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VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]);
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VOinIMU = IMUinBody.inverse().compose(VOinBody);
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%% Read data and change coordinate frame of GPS and VO measurements to IMU frame
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disp('-- Reading sensor data from file')
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% IMU data
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IMU_data = importdata('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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% VO data
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VO_data = importdata('KittiRelativePose.txt');
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VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2);
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% Merge relative pose fields and convert to Pose3
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logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ];
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logposes = num2cell(logposes, 2);
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relposes = arrayfun(@(x) {gtsam.Pose3.Expmap(x{:}')}, logposes);
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relposes = arrayfun(@(x) {VOinIMU.compose(x{:}).compose(VOinIMU.inverse())}, relposes);
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[VO_data.RelativePose] = deal(relposes{:});
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VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' });
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noiseModelVO = noiseModel.Diagonal.Sigmas([ VO_metadata.RotationSigma * [1;1;1]; VO_metadata.TranslationSigma * [1;1;1] ]);
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clear logposes relposes
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%% Get initial conditions for the estimated trajectory
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currentPoseGlobal = Pose3;
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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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sigma_init_x = noiseModel.Isotropic.Sigmas([ 1.0; 1.0; 0.01; 0.01; 0.01; 0.01 ]);
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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 * ones(3,1); IMU_metadata.GyroscopeBiasSigma * 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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timestamps = [VO_data.Time]';
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timestamps = timestamps(15:end,:); % there seem to be issues with the initial IMU measurements
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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 100 steps')
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for measurementIndex = 1:length(timestamps)
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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 = timestamps(measurementIndex, 1);
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if measurementIndex == 1
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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(PriorFactorLieVector(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 = timestamps(measurementIndex-1, 1);
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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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% LC: sigma_init_b is wrong: this should be some uncertainty on bias evolution 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 VO factor
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VOpose = VO_data(measurementIndex).RelativePose;
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newFactors.add(BetweenFactorPose3(currentPoseKey - 1, currentPoseKey, VOpose, noiseModelVO));
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% Add initial value
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newValues.insert(currentPoseKey, currentPoseGlobal.compose(VOpose));
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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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if rem(measurementIndex,100)==0 % plot every 100 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+VO)')
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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 % end main loop
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disp('-- Reached end of sensor data')
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