192 lines
8.8 KiB
Matlab
192 lines
8.8 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(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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% VO metadata
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VO_metadata = importdata(findExampleDataFile('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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% GPS metadata
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GPS_metadata = importdata(findExampleDataFile('KittiGps_metadata.txt'));
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GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2);
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GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz;
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GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]);
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GPSinIMU = IMUinBody.inverse().compose(GPSinBody);
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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(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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%IMU_data = rmfield(IMU_data, { 'accelX' 'accelY' 'accelZ' 'omegaX' 'omegaY' 'omegaZ' });
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clear imum
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% VO data
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VO_data = importdata(findExampleDataFile('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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% % % GPS data
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% % GPS_data = importdata('KittiGps.txt');
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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 = 1;
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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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currentPoseGlobal = Pose3;
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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.Sigma(6, 0.01);
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sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
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sigma_init_b = noiseModel.Isotropic.Sigma(6, 0.01);
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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('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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%% 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 = sortrows( [ ...
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[VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ...
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% % %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ...
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], 1); % this are the time-stamps at which we want to initialize a new node in the graph
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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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VOPoseKeys = []; % here we store the keys of the poses involved in VO (between) factors
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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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type = timestamps(measurementIndex, 2);
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%% bookkeeping
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if type == 1 % we store the keys corresponding to VO measurements
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VOPoseKeys = [VOPoseKeys; currentPoseKey];
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end
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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(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 = 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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if ~isempty(IMUindices) % if there are IMU measurements to integrate
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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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else % if there are no IMU measurements
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error('no IMU measurements in [t_previous, t]')
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end
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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)), sigma_init_b));
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%% Create GPS factor
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if type == 2
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newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position), ...
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noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(measurementIndex).PositionSigma).^2*ones(3,1) ])));
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end
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%% Create VO factor
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if type == 1
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VOpose = VO_data(measurementIndex).RelativePose;
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newFactors.add(BetweenFactorPose3(VOPoseKeys(end-1), VOPoseKeys(end), VOpose, noiseModelVO));
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end
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% Add initial value
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% newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position));
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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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% 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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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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