Working IMUKitti example with VO only (slow!)
Luca Carlone
parent
18a72718aa
commit
d46902ea06
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@ -6,24 +6,21 @@ import gtsam.*;
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%% Read metadata and compute relative sensor pose transforms
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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([
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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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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 = 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([
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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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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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GPS_metadata = importdata('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([
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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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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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VOinIMU = IMUinBody.inverse().compose(VOinBody);
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GPSinIMU = IMUinBody.inverse().compose(GPSinBody);
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@ -86,74 +83,85 @@ factors.add(PriorFactorLieVector(symbol('v',0), ...
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factors.add(PriorFactorConstantBias(symbol('b',0), ...
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imuBias.ConstantBias(bias_acc,bias_omega), noiseModel.Isotropic.Sigma(6, sigma_init_b)));
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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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% lastTime = 0; TODO: delete?
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% lastIndex = 0; TODO: delete?
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currentSummarizedMeasurement = [];
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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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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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[IMU_data.Time]' 3*ones(length([IMU_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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t_previous = 0;
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poseIndex = 0;
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for measurementIndex = 1:size(times,1)
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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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% t_previous = 0;%LC
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% poseIndex = 0;%LC
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% currentPose = Pose3; %LC
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position= []; %LC
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for measurementIndex = 1:size(timestamps,1)
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measurementIndex
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% currentPose = currentPose.compose(VO_data(measurementIndex).RelativePose);%LC
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%
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% position(measurementIndex,:) = currentPose.translation.vector;%LC
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% At each non=IMU measurement we initialize a new node in the graph
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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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t = times(measurementIndex, 1);
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type = times(measurementIndex, 2);
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t = timestamps(measurementIndex, 1);
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type = timestamps(measurementIndex, 2);
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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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% if type == 3
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% % Integrate IMU
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%
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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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%
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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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%
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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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%
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% % Reset summarized measurement
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% currentSummarizedMeasurement = [];
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%
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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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%
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% poseIndex = poseIndex + 1;
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% end
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% =======================================================================
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@ -178,46 +186,49 @@ for measurementIndex = 1:size(times,1)
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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.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.Time] == t, 1); % the row of the IMU_data matrix that we have to integrate
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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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factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO));
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lastVOPoseKey = currentPoseKey;
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end
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% =======================================================================
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disp('TODO: add values')
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% values.insert(, initialPose);
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% values.insert(symbol('v',lastIndex+1), initialVel);
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%% Update solver
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% =======================================================================
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isam.update(factors, values);
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factors = NonlinearFactorGraph;
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values = Values;
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isam.calculateEstimate(currentPoseKey);
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% M = isam.marginalCovariance(key_pose);
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% =======================================================================
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previousPoseKey = currentPoseKey;
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previousVelKey = currentVelKey;
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t_previous = t;
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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.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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%
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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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%
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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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%
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% % add factor
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% disp('TODO: is the VO noise right?')
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% factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO));
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%
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% lastVOPoseKey = currentPoseKey;
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% end
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% % =======================================================================
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%
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% disp('TODO: add values')
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% % values.insert(, initialPose);
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% % values.insert(symbol('v',lastIndex+1), initialVel);
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%
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% %% Update solver
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% % =======================================================================
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% isam.update(factors, values);
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% factors = NonlinearFactorGraph;
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% values = Values;
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%
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% isam.calculateEstimate(currentPoseKey);
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% % M = isam.marginalCovariance(key_pose);
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% % =======================================================================
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%
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% previousPoseKey = currentPoseKey;
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% previousVelKey = currentVelKey;
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% t_previous = t;
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end
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disp('TODO: display results')
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figure
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plot(position(:,1),position(:,2))
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% figure(1)
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% hold on;
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% plot(positions(1,:), positions(2,:), '-b');
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@ -0,0 +1,190 @@
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close all
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clc
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import gtsam.*;
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%% Read metadata and compute relative sensor pose transforms
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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 = 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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GPS_metadata = importdata('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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VOinIMU = IMUinBody.inverse().compose(VOinBody);
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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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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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%IMU_data = rmfield(IMU_data, { 'accelX' 'accelY' 'accelZ' 'omegaX' 'omegaY' 'omegaZ' });
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sigma_init_x = noiseModel.Diagonal.Precisions([1;1;1; 1;1;1]);
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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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clear imum
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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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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 = 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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%% 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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%[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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timestamps = timestamps(15:end,:);
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VOPoseKeys = [];
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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(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([IMU_data.Time] >= t_previous & [IMU_data.Time] <= 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));
|
||||
|
||||
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
|
||||
Loading…
Reference in New Issue