Fixed GPS Kitti example, VO works but bad results
Luca Carlone
parent
7a027be7e5
commit
a518dae06a
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@ -1,237 +0,0 @@
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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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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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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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%% 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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currentPoseGlobal = Pose3; % initial pose is the reference frame (navigation frame)
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currentVelocityGlobal = [0;0;0]; % the vehicle is stationary at the beginning
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bias_acc = [0;0;0]; % we initialize accelerometer biases to zero
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bias_omega = [0;0;0]; % we initialize gyro biases to zero
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%% Solver object
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isamParams = ISAM2Params;
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isamParams.setRelinearizeSkip(1);
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isam = gtsam.ISAM2(isamParams);
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%% create nonlinear factor graph
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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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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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% Prior on initial velocity
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factors.add(PriorFactorLieVector(symbol('v',0), ...
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LieVector(currentVelocityGlobal), noiseModel.Isotropic.Sigma(3, sigma_init_v)));
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% Prior on initial bias
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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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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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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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%% 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 = 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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%
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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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%% 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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%
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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.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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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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% plot3DTrajectory(isam.calculateEstimate, 'g-');
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% axis equal;
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% legend('true trajectory', 'traj integrated in body', 'traj integrated in nav')
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@ -0,0 +1,191 @@
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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('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('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('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('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('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 = 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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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(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(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), ...
|
||||
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
|
||||
|
|
@ -0,0 +1,149 @@
|
|||
close all
|
||||
clc
|
||||
|
||||
import gtsam.*;
|
||||
disp('Example of application of ISAM2 for GPS-aided 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
|
||||
|
||||
% GPS metadata
|
||||
GPS_metadata = importdata('KittiRelativePose_metadata.txt');
|
||||
GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2);
|
||||
|
||||
%% Read data
|
||||
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{:});
|
||||
clear imum
|
||||
|
||||
% GPS data
|
||||
GPS_data = importdata('Gps_converted.txt');
|
||||
GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2);
|
||||
for i = 1:numel(GPS_data)
|
||||
GPS_data(i).Position = gtsam.Point3(GPS_data(i).X, GPS_data(i).Y, GPS_data(i).Z);
|
||||
end
|
||||
noiseModelGPS = noiseModel.Diagonal.Precisions([ [0;0;0]; 1.0/0.07 * [1;1;1] ]);
|
||||
firstGPSPose = 2;
|
||||
GPSskip = 10; % Skip this many GPS measurements each time
|
||||
|
||||
%% Get initial conditions for the estimated trajectory
|
||||
currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame)
|
||||
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.Precisions([ 0.0; 0.0; 0.0; 1; 1; 1 ]);
|
||||
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
|
||||
sigma_init_b = noiseModel.Isotropic.Sigmas([ 0.100; 0.100; 0.100; 5.00e-05; 5.00e-05; 5.00e-05 ]);
|
||||
sigma_between_b = [ IMU_metadata.AccelerometerBiasSigma * ones(3,1); IMU_metadata.GyroscopeBiasSigma * ones(3,1) ];
|
||||
g = [0;0;-9.8];
|
||||
w_coriolis = [0;0;0];
|
||||
|
||||
%% Solver object
|
||||
isamParams = ISAM2Params;
|
||||
isamParams.setFactorization('CHOLESKY');
|
||||
isamParams.setRelinearizeSkip(10);
|
||||
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
|
||||
IMUtimes = [IMU_data.Time];
|
||||
|
||||
disp('-- Starting main loop: inference is performed at each time step, but we plot trajectory every 10 steps')
|
||||
|
||||
for measurementIndex = firstGPSPose:length(GPS_data)
|
||||
|
||||
% 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 = GPS_data(measurementIndex, 1).Time;
|
||||
|
||||
if measurementIndex == firstGPSPose
|
||||
%% 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 = GPS_data(measurementIndex-1, 1).Time;
|
||||
%% Summarize IMU data between the previous GPS measurement and now
|
||||
IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t);
|
||||
|
||||
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));
|
||||
|
||||
% Bias evolution as given in the IMU metadata
|
||||
newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), ...
|
||||
noiseModel.Diagonal.Sigmas(sqrt(numel(IMUindices)) * sigma_between_b)));
|
||||
|
||||
% Create GPS factor
|
||||
GPSPose = Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position);
|
||||
if mod(measurementIndex, GPSskip) == 0
|
||||
newFactors.add(PriorFactorPose3(currentPoseKey, GPSPose, noiseModelGPS));
|
||||
end
|
||||
|
||||
% Add initial value
|
||||
newValues.insert(currentPoseKey, GPSPose);
|
||||
newValues.insert(currentVelKey, currentVelocityGlobal);
|
||||
newValues.insert(currentBiasKey, currentBias);
|
||||
|
||||
% Update solver
|
||||
% =======================================================================
|
||||
% We accumulate 2*GPSskip GPS measurements before updating the solver at
|
||||
% first so that the heading becomes observable.
|
||||
if measurementIndex > firstGPSPose + 2*GPSskip
|
||||
isam.update(newFactors, newValues);
|
||||
newFactors = NonlinearFactorGraph;
|
||||
newValues = Values;
|
||||
|
||||
if rem(measurementIndex,10)==0 % plot every 10 time steps
|
||||
cla;
|
||||
plot3DTrajectory(isam.calculateEstimate, 'g-');
|
||||
title('Estimated trajectory using ISAM2 (IMU+GPS)')
|
||||
xlabel('[m]')
|
||||
ylabel('[m]')
|
||||
zlabel('[m]')
|
||||
axis equal
|
||||
drawnow;
|
||||
end
|
||||
% =======================================================================
|
||||
currentPoseGlobal = isam.calculateEstimate(currentPoseKey);
|
||||
currentVelocityGlobal = isam.calculateEstimate(currentVelKey);
|
||||
currentBias = isam.calculateEstimate(currentBiasKey);
|
||||
end
|
||||
end
|
||||
|
||||
end % end main loop
|
||||
|
||||
disp('-- Reached end of sensor data')
|
||||
|
|
@ -1,126 +0,0 @@
|
|||
%close all
|
||||
%clc
|
||||
|
||||
import gtsam.*;
|
||||
|
||||
%% Read data
|
||||
IMU_metadata = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu_metadata.txt'));
|
||||
IMU_data = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu.txt'));
|
||||
% Make text file column headers into struct fields
|
||||
IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2);
|
||||
IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2);
|
||||
|
||||
GPS_metadata = importdata(gtsam.findExampleDataFile('KittiGps_metadata.txt'));
|
||||
GPS_data = importdata(gtsam.findExampleDataFile('KittiGps.txt'));
|
||||
% Make text file column headers into struct fields
|
||||
GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2);
|
||||
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 = 2;
|
||||
|
||||
%% Get initial conditions for the estimated trajectory
|
||||
currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame)
|
||||
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;
|
||||
|
||||
%% Create initial estimate and prior on initial pose, velocity, and biases
|
||||
newValues.insert(symbol('x',firstGPSPose), currentPoseGlobal);
|
||||
newValues.insert(symbol('v',firstGPSPose), currentVelocityGlobal);
|
||||
newValues.insert(symbol('b',1), currentBias);
|
||||
|
||||
sigma_init_x = noiseModel.Diagonal.Precisions([0;0;0; 1;1;1]);
|
||||
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
|
||||
sigma_init_b = noiseModel.Isotropic.Sigma(6, 0.01);
|
||||
|
||||
newFactors.add(PriorFactorPose3(symbol('x',firstGPSPose), currentPoseGlobal, sigma_init_x));
|
||||
newFactors.add(PriorFactorLieVector(symbol('v',firstGPSPose), currentVelocityGlobal, sigma_init_v));
|
||||
newFactors.add(PriorFactorConstantBias(symbol('b',1), currentBias, 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
|
||||
|
||||
for poseIndex = firstGPSPose:length(GPS_data)
|
||||
% At each non=IMU measurement we initialize a new node in the graph
|
||||
currentPoseKey = symbol('x',poseIndex);
|
||||
currentVelKey = symbol('v',poseIndex);
|
||||
currentBiasKey = symbol('b',1);
|
||||
|
||||
if poseIndex > firstGPSPose
|
||||
% Summarize IMU data between the previous GPS measurement and now
|
||||
IMUindices = find([IMU_data.Time] > GPS_data(poseIndex-1).Time ...
|
||||
& [IMU_data.Time] <= GPS_data(poseIndex).Time);
|
||||
|
||||
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, [0;0;-9.8], [0;0;0]));
|
||||
|
||||
% Create GPS factor
|
||||
newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position), ...
|
||||
noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(poseIndex).PositionSigma).^2*ones(3,1) ])));
|
||||
|
||||
% Add initial value
|
||||
newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position));
|
||||
newValues.insert(currentVelKey, currentVelocityGlobal);
|
||||
%newValues.insert(currentBiasKey, currentBias);
|
||||
|
||||
% Update solver
|
||||
% =======================================================================
|
||||
isam.update(newFactors, newValues);
|
||||
newFactors = NonlinearFactorGraph;
|
||||
newValues = Values;
|
||||
|
||||
cla;
|
||||
plot3DTrajectory(isam.calculateEstimate, 'g-');
|
||||
drawnow;
|
||||
% =======================================================================
|
||||
|
||||
currentPoseGlobal = isam.calculateEstimate(currentPoseKey);
|
||||
currentVelocityGlobal = isam.calculateEstimate(currentVelKey);
|
||||
currentBias = isam.calculateEstimate(currentBiasKey);
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
disp('TODO: display results')
|
||||
% figure(1)
|
||||
% hold on;
|
||||
% plot(positions(1,:), positions(2,:), '-b');
|
||||
% plot3DTrajectory(isam.calculateEstimate, 'g-');
|
||||
% axis equal;
|
||||
% legend('true trajectory', 'traj integrated in body', 'traj integrated in nav')
|
||||
|
|
@ -2,9 +2,11 @@ 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;
|
||||
|
|
@ -20,25 +22,13 @@ VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.B
|
|||
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' });
|
||||
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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||||
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||||
% VO data
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||||
|
|
@ -54,32 +44,21 @@ 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
|
||||
|
||||
% GPS data
|
||||
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
|
||||
[ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] );
|
||||
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
|
||||
% firstGPSPose = 1;
|
||||
|
||||
%% Get initial conditions for the estimated trajectory
|
||||
% 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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sigma_init_x = noiseModel.Isotropic.Sigmas([ 1.0; 1.0; 0.01; 0.01; 0.01; 0.01 ]);
|
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sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
|
||||
sigma_init_b = noiseModel.Isotropic.Sigmas([ 0.100; 0.100; 0.100; 5.00e-05; 5.00e-05; 5.00e-05 ]);
|
||||
sigma_between_b = [ IMU_metadata.AccelerometerBiasSigma * ones(3,1); IMU_metadata.GyroscopeBiasSigma * ones(3,1) ];
|
||||
g = [0;0;-9.8];
|
||||
w_coriolis = [0;0;0];
|
||||
|
||||
%% Solver object
|
||||
isamParams = ISAM2Params;
|
||||
isamParams.setFactorization('QR');
|
||||
isamParams.setRelinearizeSkip(1);
|
||||
isamParams.setFactorization('CHOLESKY');
|
||||
isamParams.setRelinearizeSkip(10);
|
||||
isam = gtsam.ISAM2(isamParams);
|
||||
newFactors = NonlinearFactorGraph;
|
||||
newValues = Values;
|
||||
|
|
@ -88,13 +67,12 @@ newValues = Values;
|
|||
% (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 = [VO_data.Time]';
|
||||
|
||||
timestamps = timestamps(15:end,:); % there seem to be issues with the initial IMU measurements
|
||||
VOPoseKeys = []; % here we store the keys of the poses involved in VO (between) factors
|
||||
IMUtimes = [IMU_data.Time];
|
||||
|
||||
disp('-- Starting main loop: inference is performed at each time step, but we plot trajectory every 100 steps')
|
||||
|
||||
for measurementIndex = 1:length(timestamps)
|
||||
|
||||
|
|
@ -103,12 +81,6 @@ for measurementIndex = 1:length(timestamps)
|
|||
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
|
||||
|
|
@ -121,48 +93,35 @@ for measurementIndex = 1:length(timestamps)
|
|||
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);
|
||||
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]')
|
||||
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));
|
||||
|
||||
% 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
|
||||
newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), ...
|
||||
noiseModel.Diagonal.Sigmas(sqrt(numel(IMUindices)) * sigma_between_b)));
|
||||
|
||||
%% Create VO factor
|
||||
if type == 1
|
||||
VOpose = VO_data(measurementIndex).RelativePose;
|
||||
newFactors.add(BetweenFactorPose3(VOPoseKeys(end-1), VOPoseKeys(end), VOpose, noiseModelVO));
|
||||
end
|
||||
newFactors.add(BetweenFactorPose3(currentPoseKey - 1, currentPoseKey, VOpose, noiseModelVO));
|
||||
|
||||
% Add initial value
|
||||
% newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position));
|
||||
newValues.insert(currentPoseKey,currentPoseGlobal);
|
||||
newValues.insert(currentPoseKey, currentPoseGlobal.compose(VOpose));
|
||||
newValues.insert(currentVelKey, currentVelocityGlobal);
|
||||
newValues.insert(currentBiasKey, currentBias);
|
||||
|
||||
|
|
@ -172,9 +131,13 @@ for measurementIndex = 1:length(timestamps)
|
|||
newFactors = NonlinearFactorGraph;
|
||||
newValues = Values;
|
||||
|
||||
if rem(measurementIndex,20)==0 % plot every 20 time steps
|
||||
if rem(measurementIndex,100)==0 % plot every 100 time steps
|
||||
cla;
|
||||
plot3DTrajectory(isam.calculateEstimate, 'g-');
|
||||
title('Estimated trajectory using ISAM2 (IMU+VO)')
|
||||
xlabel('[m]')
|
||||
ylabel('[m]')
|
||||
zlabel('[m]')
|
||||
axis equal
|
||||
drawnow;
|
||||
end
|
||||
|
|
@ -185,3 +148,5 @@ for measurementIndex = 1:length(timestamps)
|
|||
end
|
||||
|
||||
end % end main loop
|
||||
|
||||
disp('-- Reached end of sensor data')
|
||||
|
|
|
|||
Loading…
Reference in New Issue