diff --git a/matlab/gtsam_examples/IMUKittiExampleVO.m b/matlab/gtsam_examples/IMUKittiExampleVO.m index 10bd33409..9541aff8d 100644 --- a/matlab/gtsam_examples/IMUKittiExampleVO.m +++ b/matlab/gtsam_examples/IMUKittiExampleVO.m @@ -4,6 +4,7 @@ clc import gtsam.*; %% Read metadata and compute relative sensor pose transforms +% IMU 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; @@ -12,20 +13,22 @@ if ~IMUinBody.equals(Pose3, 1e-5) error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; end +% VO metadata VO_metadata = importdata('KittiRelativePose_metadata.txt'); VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2); VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); +VOinIMU = IMUinBody.inverse().compose(VOinBody); +% GPS metadata GPS_metadata = importdata('KittiGps_metadata.txt'); GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz; GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]); - -VOinIMU = IMUinBody.inverse().compose(VOinBody); GPSinIMU = IMUinBody.inverse().compose(GPSinBody); %% Read data and change coordinate frame of GPS and VO measurements to IMU frame +% IMU data 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); @@ -38,6 +41,7 @@ g = [0;0;-9.8]; w_coriolis = [0;0;0]; clear imum +% VO data VO_data = importdata('KittiRelativePose.txt'); VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2); % Merge relative pose fields and convert to Pose3 @@ -50,23 +54,21 @@ VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' }); noiseModelVO = noiseModel.Diagonal.Sigmas([ VO_metadata.RotationSigma * [1;1;1]; VO_metadata.TranslationSigma * [1;1;1] ]); clear logposes relposes +% GPS data GPS_data = importdata('KittiGps.txt'); GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); - % Convert GPS from lat/long to meters [ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] ); for i = 1:numel(x) GPS_data(i).Position = gtsam.Point3(x(i), y(i), GPS_data(i).Altitude); end - % % Calculate GPS sigma in meters % [ xSig, ySig, ~ ] = deg2utm( [GPS_data.Latitude] + [GPS_data.PositionSigma], ... % [GPS_data.Longitude] + [GPS_data.PositionSigma]); % xSig = xSig - x; % ySig = ySig - y; - %% Start at time of first GPS measurement -firstGPSPose = 1; +% firstGPSPose = 1; %% Get initial conditions for the estimated trajectory % currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame) @@ -82,7 +84,6 @@ isam = gtsam.ISAM2(isamParams); newFactors = NonlinearFactorGraph; newValues = Values; - %% Main loop: % (1) we read the measurements % (2) we create the corresponding factors in the graph @@ -90,12 +91,10 @@ newValues = Values; timestamps = sortrows( [ ... [VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ... %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ... - %[IMU_data.Time]' 3*ones(length([IMU_data.Time]), 1); ... ], 1); % this are the time-stamps at which we want to initialize a new node in the graph -timestamps = timestamps(15:end,:); - -VOPoseKeys = []; +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 for measurementIndex = 1:length(timestamps) @@ -128,7 +127,7 @@ for measurementIndex = 1:length(timestamps) 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 ]; @@ -146,6 +145,7 @@ for measurementIndex = 1:length(timestamps) 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 @@ -161,7 +161,7 @@ for measurementIndex = 1:length(timestamps) end % Add initial value - %newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position)); + % newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position)); newValues.insert(currentPoseKey,currentPoseGlobal); newValues.insert(currentVelKey, currentVelocityGlobal); newValues.insert(currentBiasKey, currentBias); @@ -172,19 +172,16 @@ for measurementIndex = 1:length(timestamps) newFactors = NonlinearFactorGraph; newValues = Values; - if rem(measurementIndex,20)==0 + if rem(measurementIndex,20)==0 % plot every 20 time steps cla; plot3DTrajectory(isam.calculateEstimate, 'g-'); axis equal drawnow; end - % ======================================================================= - + % ======================================================================= currentPoseGlobal = isam.calculateEstimate(currentPoseKey); currentVelocityGlobal = isam.calculateEstimate(currentVelKey); - currentBias = isam.calculateEstimate(currentBiasKey); - + currentBias = isam.calculateEstimate(currentBiasKey); end - - + end % end main loop