IMUKittiExample: added infrastructure for reading and processing data - work in progress
Luca Carlone
parent
c9c2025d52
commit
334d71ce51
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@ -9,6 +9,9 @@ IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders,
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IMUinBody = Pose3.Expmap([
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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 = importdata('KittiRelativePose_metadata.txt');
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VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2);
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@ -38,19 +41,14 @@ 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{1}')}, logposes);
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% TODO: convert to IMU frame %relposes = arrayfun(
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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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clear logposes relposes
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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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%%
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SummaryTemplate = gtsam.ImuFactorPreintegratedMeasurements( ...
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gtsam.imuBias.ConstantBias([0;0;0], [0;0;0]), ...
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1e-3 * eye(3), 1e-3 * eye(3), 1e-3 * eye(3));
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%% Set initial conditions for the estimated trajectory
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disp('TODO: we have GPS so this initialization is not right')
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@ -69,14 +67,15 @@ factors = NonlinearFactorGraph;
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values = Values;
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%% Create prior on initial pose, velocity, and biases
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sigma_init_x = 1.0
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sigma_init_v = 1.0
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sigma_init_b = 1.0
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sigma_init_x = 1.0;
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sigma_init_v = 1.0;
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sigma_init_b = 1.0;
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values.insert(symbol('x',0), currentPoseGlobal);
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values.insert(symbol('v',0), LieVector(currentVelocityGlobal) );
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values.insert(symbol('b',0), imuBias.ConstantBias(bias_acc,bias_omega) );
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disp('TODO: we have GPS so this initialization is not right')
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% Prior on initial pose
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factors.add(PriorFactorPose3(symbol('x',0), ...
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currentPoseGlobal, noiseModel.Isotropic.Sigma(6, sigma_init_x)));
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@ -92,83 +91,104 @@ factors.add(PriorFactorConstantBias(symbol('b',0), ...
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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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i = 2;
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lastTime = 0;
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lastIndex = 0;
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currentSummarizedMeasurement = ImuFactorPreintegratedMeasurements(summaryTemplate);
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% lastTime = 0; TODO: delete?
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% lastIndex = 0; TODO: delete?
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currentSummarizedMeasurement = [];
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times = sort([VO_data(:,1); GPS_data(:,1)]); % this are the time-stamps at which we want to initialize a new node in the graph
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IMU_times = IMU_data(:,1);
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% Measurement types:
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% 1: VO
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% 2: GPS
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% 3: IMU
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times = 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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[IMU_data.Time]' 3*ones(length([IMU_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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disp('TODO: still needed to take care of the initial time')
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for t = times
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t_previous = 0;
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poseIndex = 0;
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for measurementIndex = 1:size(times,1)
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% At each non=IMU measurement we initialize a new node in the graph
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currentIndex = find( times == t );
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currentPoseKey = symbol('x',currentIndex);
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currentVelKey = symbol('v',currentIndex);
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currentBiasKey = symbol('b',currentIndex);
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currentPoseKey = symbol('x',poseIndex);
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currentVelKey = symbol('v',poseIndex);
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currentBiasKey = symbol('b',poseIndex);
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%% add preintegrated IMU factor between previous state and current state
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% =======================================================================
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IMUbetweenTimesIndices = find( IMU_times>+t_previous & IMU_times<= t);
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% all imu measurements occurred between t_previous and t
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t = times(measurementIndex, 1);
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type = times(measurementIndex, 2);
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% we assume that each row of the IMU.txt file has the following structure:
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% timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z
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disp('TODO: We want don t want to preintegrate with zero bias, but to use the most recent estimate')
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currentSummarizedMeasurement = ImuFactorPreintegratedMeasurements(summaryTemplate);
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for i=1:length(IMUbetweenTimesIndices)
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index = IMUbetweenTimesIndices(i); % the row of the IMU_data matrix that we have to integrate
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deltaT = IMU_data(index,2);
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accMeas = IMU_data(index,3:5);
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omegaMeas = IMU_data(index,6:8);
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if type == 3
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% Integrate IMU
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if isempty(currentSummarizedMeasurement)
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% Create initial empty summarized measurement
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% we assume that each row of the IMU.txt file has the following structure:
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% timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z
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currentBias = isam.calculateEstimate(currentBiasKey - 1);
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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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end
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% Accumulate preintegrated measurement
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deltaT = IMU_data(index).dt;
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accMeas = IMU_data(index).acc_omega(1:3);
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omegaMeas = IMU_data(index).acc_omega(4:6);
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currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT);
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else
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% Create IMU factor
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factors.add(ImuFactor( ...
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currentPoseKey-1, currentVelKey-1, ...
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currentPoseKey, currentVelKey, ...
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currentBiasKey-1, currentSummarizedMeasurement, g, cor_v, ...
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currentSummarizedMeasurement.PreintMeasCov));
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% Reset summarized measurement
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currentSummarizedMeasurement = [];
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if type == 1
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% Create VO factor
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elseif type == 2
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% Create GPS factor
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end
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poseIndex = poseIndex + 1;
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end
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disp('TODO: is the imu noise right?')
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% Create IMU factor
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factors.add(ImuFactor( ...
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previousPoseKey, previousVelKey, ...
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currentPoseKey, currentVelKey, ...
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currentBiasKey, currentSummarizedMeasurement, g, cor_v, ...
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noiseModel.Isotropic.Sigma(9, 1e-6)));
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% =======================================================================
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%% add factor corresponding to GPS measurements (if available at the current time)
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% =======================================================================
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if isempty( find(GPS_data(:,1) == t ) ) == 0 % it is a GPS measurement
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if length( find(GPS_data(:,1)) ) > 1
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error('more GPS measurements at the same time stamp: it should be an error')
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end
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index = find(GPS_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate
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GPSmeas = GPS_data(index,2:4);
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noiseModelGPS = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x))
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% add factor
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disp('TODO: is the GPS noise right?')
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factors.add(PriorFactor???(currentPoseKey, GPSmeas, noiseModelGPS) );
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end
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% % =======================================================================
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% if isempty( find(GPS_data(:,1) == t ) ) == 0 % it is a GPS measurement
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% if length( find(GPS_data(:,1)) ) > 1
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% error('more GPS measurements at the same time stamp: it should be an error')
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% end
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%
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% index = find(GPS_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate
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% GPSmeas = GPS_data(index,2:4);
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%
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% noiseModelGPS = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x))
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%
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% % add factor
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% disp('TODO: is the GPS noise right?')
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% factors.add(PriorFactor???(currentPoseKey, GPSmeas, noiseModelGPS) );
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% end
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% =======================================================================
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%% add factor corresponding to VO measurements (if available at the current time)
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% =======================================================================
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if isempty( find(VO_data(:,1) == t ) )== 0 % it is a GPS measurement
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if length( find(VO_data(:,1)) ) > 1
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if isempty( find([VO_data.Time] == t, 1) )== 0 % it is a GPS measurement
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if length( find([VO_data.Time] == t) ) > 1
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error('more VO measurements at the same time stamp: it should be an error')
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end
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index = find( VO_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate
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VOmeas_pos = VO_data(index,2:4)';
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VOmeas_ang = VO_data(index,5:7)';
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index = find([VO_data.Time] == t, 1); % the row of the IMU_data matrix that we have to integrate
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VOpose = Pose3( Rot3(VOmeas_ang) , Point3(VOmeas_pos) );
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noiseModelVO = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x))
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VOpose = VO_data(index).RelativePose;
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noiseModelVO = noiseModel.Diagonal.Sigmas([ IMU_metadata.RotationSigma * [1;1;1]; IMU_metadata.TranslationSigma * [1;1;1] ]);
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% add factor
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disp('TODO: is the VO noise right?')
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