close all clc import gtsam.*; 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)') %% Read metadata and compute relative sensor pose transforms % IMU metadata disp('-- Reading sensor metadata') IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt'); IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz; IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]); if ~IMUinBody.equals(Pose3, 1e-5) error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; end % VO metadata VO_metadata = importdata('KittiRelativePose_metadata.txt'); VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2); VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); VOinIMU = IMUinBody.inverse().compose(VOinBody); % GPS metadata GPS_metadata = importdata('KittiGps_metadata.txt'); GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz; GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]); GPSinIMU = IMUinBody.inverse().compose(GPSinBody); %% Read data and change coordinate frame of GPS and VO measurements to IMU frame disp('-- Reading sensor data from file') % IMU data IMU_data = importdata('KittiEquivBiasedImu.txt'); IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); 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); [IMU_data.acc_omega] = deal(imum{:}); %IMU_data = rmfield(IMU_data, { 'accelX' 'accelY' 'accelZ' 'omegaX' 'omegaY' 'omegaZ' }); clear imum % VO data VO_data = importdata('KittiRelativePose.txt'); VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2); % Merge relative pose fields and convert to Pose3 logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ]; logposes = num2cell(logposes, 2); relposes = arrayfun(@(x) {gtsam.Pose3.Expmap(x{:}')}, logposes); relposes = arrayfun(@(x) {VOinIMU.compose(x{:}).compose(VOinIMU.inverse())}, relposes); [VO_data.RelativePose] = deal(relposes{:}); VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' }); noiseModelVO = noiseModel.Diagonal.Sigmas([ VO_metadata.RotationSigma * [1;1;1]; VO_metadata.TranslationSigma * [1;1;1] ]); clear logposes relposes % % % GPS data % % GPS_data = importdata('KittiGps.txt'); % % GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); % % % Convert GPS from lat/long to meters % % [ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] ); % % for i = 1:numel(x) % % GPS_data(i).Position = gtsam.Point3(x(i), y(i), GPS_data(i).Altitude); % % end % % % % Calculate GPS sigma in meters % % % [ xSig, ySig, ~ ] = deg2utm( [GPS_data.Latitude] + [GPS_data.PositionSigma], ... % % % [GPS_data.Longitude] + [GPS_data.PositionSigma]); % % % xSig = xSig - x; % % % ySig = ySig - y; % % %% Start at time of first GPS measurement % % % firstGPSPose = 1; %% Get initial conditions for the estimated trajectory % % % currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame) currentPoseGlobal = Pose3; currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); sigma_init_x = noiseModel.Isotropic.Sigma(6, 0.01); sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0); sigma_init_b = noiseModel.Isotropic.Sigma(6, 0.01); g = [0;0;-9.8]; w_coriolis = [0;0;0]; %% Solver object isamParams = ISAM2Params; isamParams.setFactorization('QR'); isamParams.setRelinearizeSkip(1); isam = gtsam.ISAM2(isamParams); newFactors = NonlinearFactorGraph; newValues = Values; %% Main loop: % (1) we read the measurements % (2) we create the corresponding factors in the graph % (3) we solve the graph to obtain and optimal estimate of robot trajectory timestamps = sortrows( [ ... [VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ... % % %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ... ], 1); % this are the time-stamps at which we want to initialize a new node in the graph timestamps = timestamps(15:end,:); % there seem to be issues with the initial IMU measurements IMUtimes = [IMU_data.Time]; VOPoseKeys = []; % here we store the keys of the poses involved in VO (between) factors for measurementIndex = 1:length(timestamps) % At each non=IMU measurement we initialize a new node in the graph currentPoseKey = symbol('x',measurementIndex); currentVelKey = symbol('v',measurementIndex); currentBiasKey = symbol('b',measurementIndex); t = timestamps(measurementIndex, 1); type = timestamps(measurementIndex, 2); %% bookkeeping if type == 1 % we store the keys corresponding to VO measurements VOPoseKeys = [VOPoseKeys; currentPoseKey]; end if measurementIndex == 1 %% Create initial estimate and prior on initial pose, velocity, and biases newValues.insert(currentPoseKey, currentPoseGlobal); newValues.insert(currentVelKey, currentVelocityGlobal); newValues.insert(currentBiasKey, currentBias); newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x)); newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v)); newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b)); else t_previous = timestamps(measurementIndex-1, 1); %% Summarize IMU data between the previous GPS measurement and now IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t); if ~isempty(IMUindices) % if there are IMU measurements to integrate currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); for imuIndex = IMUindices accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ]; omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ]; deltaT = IMU_data(imuIndex).dt; currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); end % Create IMU factor newFactors.add(ImuFactor( ... currentPoseKey-1, currentVelKey-1, ... currentPoseKey, currentVelKey, ... currentBiasKey, currentSummarizedMeasurement, g, w_coriolis)); else % if there are no IMU measurements error('no IMU measurements in [t_previous, t]') end % LC: sigma_init_b is wrong: this should be some uncertainty on bias evolution given in the IMU metadata newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), sigma_init_b)); %% Create GPS factor if type == 2 newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position), ... noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(measurementIndex).PositionSigma).^2*ones(3,1) ]))); end %% Create VO factor if type == 1 VOpose = VO_data(measurementIndex).RelativePose; newFactors.add(BetweenFactorPose3(VOPoseKeys(end-1), VOPoseKeys(end), VOpose, noiseModelVO)); end % Add initial value % newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position)); newValues.insert(currentPoseKey,currentPoseGlobal); newValues.insert(currentVelKey, currentVelocityGlobal); newValues.insert(currentBiasKey, currentBias); % Update solver % ======================================================================= isam.update(newFactors, newValues); newFactors = NonlinearFactorGraph; newValues = Values; if rem(measurementIndex,100)==0 % plot every 100 time steps cla; plot3DTrajectory(isam.calculateEstimate, 'g-'); axis equal drawnow; end % ======================================================================= currentPoseGlobal = isam.calculateEstimate(currentPoseKey); currentVelocityGlobal = isam.calculateEstimate(currentVelKey); currentBias = isam.calculateEstimate(currentBiasKey); end end % end main loop