From d46902ea062a068e32f6d0c9c3a7cc138c7af9ef Mon Sep 17 00:00:00 2001 From: Luca Carlone Date: Sun, 11 Aug 2013 22:45:58 +0000 Subject: [PATCH] Working IMUKitti example with VO only (slow!) --- matlab/gtsam_examples/IMUKittiExample.m | 209 ++++++++++++---------- matlab/gtsam_examples/IMUKittiExampleVO.m | 190 ++++++++++++++++++++ 2 files changed, 300 insertions(+), 99 deletions(-) create mode 100644 matlab/gtsam_examples/IMUKittiExampleVO.m diff --git a/matlab/gtsam_examples/IMUKittiExample.m b/matlab/gtsam_examples/IMUKittiExample.m index 92239353b..ec536bc17 100644 --- a/matlab/gtsam_examples/IMUKittiExample.m +++ b/matlab/gtsam_examples/IMUKittiExample.m @@ -6,24 +6,21 @@ import gtsam.*; %% Read metadata and compute relative sensor pose transforms 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; ]); +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 = 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; ]); +VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; + VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); 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; ]); +GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz; + GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]); VOinIMU = IMUinBody.inverse().compose(VOinBody); GPSinIMU = IMUinBody.inverse().compose(GPSinBody); @@ -86,74 +83,85 @@ factors.add(PriorFactorLieVector(symbol('v',0), ... factors.add(PriorFactorConstantBias(symbol('b',0), ... imuBias.ConstantBias(bias_acc,bias_omega), noiseModel.Isotropic.Sigma(6, sigma_init_b))); -%% 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 - -% lastTime = 0; TODO: delete? -% lastIndex = 0; TODO: delete? currentSummarizedMeasurement = []; % Measurement types: % 1: VO % 2: GPS % 3: IMU -times = sortrows( [ ... +timestamps = sortrows( [ ... [VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ... %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ... - [IMU_data.Time]' 3*ones(length([IMU_data.Time]), 1); ... + %[IMU_data.Time]' 3*ones(length([IMU_data.Time]), 1); ... ], 1); % this are the time-stamps at which we want to initialize a new node in the graph -t_previous = 0; -poseIndex = 0; -for measurementIndex = 1:size(times,1) +%% 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 + +% t_previous = 0;%LC +% poseIndex = 0;%LC + +% currentPose = Pose3; %LC + +position= []; %LC + +for measurementIndex = 1:size(timestamps,1) + + measurementIndex + +% currentPose = currentPose.compose(VO_data(measurementIndex).RelativePose);%LC +% +% position(measurementIndex,:) = currentPose.translation.vector;%LC + + % At each non=IMU measurement we initialize a new node in the graph currentPoseKey = symbol('x',poseIndex); currentVelKey = symbol('v',poseIndex); currentBiasKey = symbol('b',poseIndex); - t = times(measurementIndex, 1); - type = times(measurementIndex, 2); + t = timestamps(measurementIndex, 1); + type = timestamps(measurementIndex, 2); - if type == 3 - % Integrate IMU - - if isempty(currentSummarizedMeasurement) - % Create initial empty summarized measurement - % we assume that each row of the IMU.txt file has the following structure: - % timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z - currentBias = isam.calculateEstimate(currentBiasKey - 1); - currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... - currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... - IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); - end - - % Accumulate preintegrated measurement - deltaT = IMU_data(index).dt; - accMeas = IMU_data(index).acc_omega(1:3); - omegaMeas = IMU_data(index).acc_omega(4:6); - currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); - - else - % Create IMU factor - factors.add(ImuFactor( ... - currentPoseKey-1, currentVelKey-1, ... - currentPoseKey, currentVelKey, ... - currentBiasKey-1, currentSummarizedMeasurement, g, cor_v, ... - currentSummarizedMeasurement.PreintMeasCov)); - - % Reset summarized measurement - currentSummarizedMeasurement = []; - - if type == 1 - % Create VO factor - elseif type == 2 - % Create GPS factor - end - - poseIndex = poseIndex + 1; - end +% if type == 3 +% % Integrate IMU +% +% if isempty(currentSummarizedMeasurement) +% % Create initial empty summarized measurement +% % we assume that each row of the IMU.txt file has the following structure: +% % timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z +% currentBias = isam.calculateEstimate(currentBiasKey - 1); +% currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... +% currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... +% IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); +% end +% +% % Accumulate preintegrated measurement +% deltaT = IMU_data(index).dt; +% accMeas = IMU_data(index).acc_omega(1:3); +% omegaMeas = IMU_data(index).acc_omega(4:6); +% currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); +% +% else +% % Create IMU factor +% factors.add(ImuFactor( ... +% currentPoseKey-1, currentVelKey-1, ... +% currentPoseKey, currentVelKey, ... +% currentBiasKey-1, currentSummarizedMeasurement, g, cor_v, ... +% currentSummarizedMeasurement.PreintMeasCov)); +% +% % Reset summarized measurement +% currentSummarizedMeasurement = []; +% +% if type == 1 +% % Create VO factor +% elseif type == 2 +% % Create GPS factor +% end +% +% poseIndex = poseIndex + 1; +% end % ======================================================================= @@ -178,46 +186,49 @@ for measurementIndex = 1:size(times,1) % ======================================================================= - %% add factor corresponding to VO measurements (if available at the current time) - % ======================================================================= - if isempty( find([VO_data.Time] == t, 1) )== 0 % it is a GPS measurement - if length( find([VO_data.Time] == t) ) > 1 - error('more VO measurements at the same time stamp: it should be an error') - end - - index = find([VO_data.Time] == t, 1); % the row of the IMU_data matrix that we have to integrate - - VOpose = VO_data(index).RelativePose; - noiseModelVO = noiseModel.Diagonal.Sigmas([ IMU_metadata.RotationSigma * [1;1;1]; IMU_metadata.TranslationSigma * [1;1;1] ]); - - % add factor - disp('TODO: is the VO noise right?') - factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO)); - - lastVOPoseKey = currentPoseKey; - end - % ======================================================================= - - disp('TODO: add values') - % values.insert(, initialPose); - % values.insert(symbol('v',lastIndex+1), initialVel); - - %% Update solver - % ======================================================================= - isam.update(factors, values); - factors = NonlinearFactorGraph; - values = Values; - - isam.calculateEstimate(currentPoseKey); - % M = isam.marginalCovariance(key_pose); - % ======================================================================= - - previousPoseKey = currentPoseKey; - previousVelKey = currentVelKey; - t_previous = t; +% %% add factor corresponding to VO measurements (if available at the current time) +% % ======================================================================= +% if isempty( find([VO_data.Time] == t, 1) )== 0 % it is a GPS measurement +% if length( find([VO_data.Time] == t) ) > 1 +% error('more VO measurements at the same time stamp: it should be an error') +% end +% +% index = find([VO_data.Time] == t, 1); % the row of the IMU_data matrix that we have to integrate +% +% VOpose = VO_data(index).RelativePose; +% noiseModelVO = noiseModel.Diagonal.Sigmas([ IMU_metadata.RotationSigma * [1;1;1]; IMU_metadata.TranslationSigma * [1;1;1] ]); +% +% % add factor +% disp('TODO: is the VO noise right?') +% factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO)); +% +% lastVOPoseKey = currentPoseKey; +% end +% % ======================================================================= +% +% disp('TODO: add values') +% % values.insert(, initialPose); +% % values.insert(symbol('v',lastIndex+1), initialVel); +% +% %% Update solver +% % ======================================================================= +% isam.update(factors, values); +% factors = NonlinearFactorGraph; +% values = Values; +% +% isam.calculateEstimate(currentPoseKey); +% % M = isam.marginalCovariance(key_pose); +% % ======================================================================= +% +% previousPoseKey = currentPoseKey; +% previousVelKey = currentVelKey; +% t_previous = t; end -disp('TODO: display results') +figure +plot(position(:,1),position(:,2)) + + % figure(1) % hold on; % plot(positions(1,:), positions(2,:), '-b'); diff --git a/matlab/gtsam_examples/IMUKittiExampleVO.m b/matlab/gtsam_examples/IMUKittiExampleVO.m new file mode 100644 index 000000000..10bd33409 --- /dev/null +++ b/matlab/gtsam_examples/IMUKittiExampleVO.m @@ -0,0 +1,190 @@ +close all +clc + +import gtsam.*; + +%% Read metadata and compute relative sensor pose transforms +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 = 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; ]); + +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; ]); + +VOinIMU = IMUinBody.inverse().compose(VOinBody); +GPSinIMU = IMUinBody.inverse().compose(GPSinBody); + +%% Read data and change coordinate frame of GPS and VO measurements to IMU frame +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' }); +sigma_init_x = noiseModel.Diagonal.Precisions([1;1;1; 1;1;1]); +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]; +clear imum + +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 = 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)); + +%% 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); ... + %[IMU_data.Time]' 3*ones(length([IMU_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,:); + +VOPoseKeys = []; + +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([IMU_data.Time] >= t_previous & [IMU_data.Time] <= 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 + + 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,20)==0 + 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