diff --git a/matlab/gtsam_examples/IMUKittiExample.m b/matlab/gtsam_examples/IMUKittiExample.m
new file mode 100644
index 000000000..4f995b7b9
--- /dev/null
+++ b/matlab/gtsam_examples/IMUKittiExample.m
@@ -0,0 +1,165 @@
+close all
+clc
+
+import gtsam.*;
+
+IMU_data = dmlread('IMU.txt');
+VO_data = dmlread('VO.txt');
+GPS_data = dmlread('GPS.txt');
+
+SummaryTemplate = gtsam.ImuFactorPreintegratedMeasurements( ...
+    gtsam.imuBias.ConstantBias([0;0;0], [0;0;0]), ...
+    1e-3 * eye(3), 1e-3 * eye(3), 1e-3 * eye(3));
+  
+%% Set initial conditions for the estimated trajectory
+disp('TODO: we have GPS so this initialization is not right')
+currentPoseGlobal = Pose3; % initial pose is the reference frame (navigation frame)
+currentVelocityGlobal = [0;0;0]; % the vehicle is stationary at the beginning
+bias_acc = [0;0;0]; % we initialize accelerometer biases to zero
+bias_omega = [0;0;0]; % we initialize gyro biases to zero
+
+%% Solver object
+isamParams = ISAM2Params;
+isamParams.setRelinearizeSkip(1);
+isam = gtsam.ISAM2(isamParams);
+
+%% create nonlinear factor graph
+factors = NonlinearFactorGraph;
+values = Values;
+
+%% Create prior on initial pose, velocity, and biases
+sigma_init_x = 1.0
+sigma_init_v = 1.0
+sigma_init_b = 1.0
+
+values.insert(symbol('x',0), currentPoseGlobal);
+values.insert(symbol('v',0), LieVector(currentVelocityGlobal) );
+values.insert(symbol('b',0), imuBias.ConstantBias(bias_acc,bias_omega) );
+
+% Prior on initial pose
+factors.add(PriorFactorPose3(symbol('x',0), ...
+  currentPoseGlobal, noiseModel.Isotropic.Sigma(6, sigma_init_x)));
+% Prior on initial velocity
+factors.add(PriorFactorLieVector(symbol('v',0), ...
+  LieVector(currentVelocityGlobal), noiseModel.Isotropic.Sigma(3, sigma_init_v)));
+% Prior on initial bias
+factors.add(PriorFactorConstantBias(symbol('b',0), ...
+  imuBias.ConstantBias(bias_acc,bias_omega), noiseModel.Isotropic.Sigma(6, sigma_init_b)));
+
+%% Main loop:
+% (1) we read the measurements
+% (2) we create the corresponding factors in the graph
+% (3) we solve the graph to obtain and optimal estimate of robot trajectory
+
+i = 2;
+lastTime = 0;
+lastIndex = 0;
+currentSummarizedMeasurement = ImuFactorPreintegratedMeasurements(summaryTemplate);
+
+times = sort([VO_data(:,1); GPS_data(:,1)]); % this are the time-stamps at which we want to initialize a new node in the graph
+IMU_times = IMU_data(:,1);
+
+disp('TODO: still needed to take care of the initial time')
+
+for t = times
+  % At each non=IMU measurement we initialize a new node in the graph
+  currentIndex = find( times == t );
+  currentPoseKey = symbol('x',currentIndex);
+  currentVelKey = symbol('v',currentIndex);
+  currentBiasKey = symbol('b',currentIndex);
+  
+  %% add preintegrated IMU factor between previous state and current state
+  % =======================================================================
+  IMUbetweenTimesIndices = find( IMU_times>+t_previous & IMU_times<= t);
+  % all imu measurements occurred between t_previous and t
+  
+  % we assume that each row of the IMU.txt file has the following structure:
+  % timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z
+  disp('TODO: We want don t want to preintegrate with zero bias, but to use the most recent estimate')
+  currentSummarizedMeasurement = ImuFactorPreintegratedMeasurements(summaryTemplate);
+  for i=1:length(IMUbetweenTimesIndices)
+    index = IMUbetweenTimesIndices(i); % the row of the IMU_data matrix that we have to integrate
+    deltaT = IMU_data(index,2);
+    accMeas = IMU_data(index,3:5);
+    omegaMeas = IMU_data(index,6:8);
+    % Accumulate preintegrated measurement
+    currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT);
+  end
+  
+  disp('TODO: is the imu noise right?')
+  % Create IMU factor
+  factors.add(ImuFactor( ...
+    previousPoseKey, previousVelKey, ...
+    currentPoseKey, currentVelKey, ...
+    currentBiasKey, currentSummarizedMeasurement, g, cor_v, ...
+    noiseModel.Isotropic.Sigma(9, 1e-6)));
+  % =======================================================================
+  
+  
+  %% add factor corresponding to GPS measurements (if available at the current time)
+  % =======================================================================
+  if isempty(  find(GPS_data(:,1) == t  ) ) == 0 % it is a GPS measurement
+    if length( find(GPS_data(:,1)) ) > 1
+      error('more GPS measurements at the same time stamp: it should be an error')
+    end
+    
+    index = find(GPS_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate
+    GPSmeas = GPS_data(index,2:4);
+    
+    noiseModelGPS = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x))
+    
+    % add factor
+    disp('TODO: is the GPS noise right?')
+    factors.add(PriorFactor???(currentPoseKey, GPSmeas, noiseModelGPS) );
+  end
+  % =======================================================================
+  
+  
+  %% add factor corresponding to VO measurements (if available at the current time)
+  % =======================================================================
+  if isempty(  find(VO_data(:,1) == t  )  )== 0 % it is a GPS measurement
+    if length( find(VO_data(:,1)) ) > 1
+      error('more VO measurements at the same time stamp: it should be an error')
+    end
+    
+    index = find( VO_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate
+    VOmeas_pos = VO_data(index,2:4)';
+    VOmeas_ang = VO_data(index,5:7)';
+    
+    VOpose = Pose3( Rot3(VOmeas_ang) , Point3(VOmeas_pos) );
+    noiseModelVO = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x))
+    
+    % add factor
+    disp('TODO: is the VO noise right?')
+    factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO));
+    
+    lastVOPoseKey = currentPoseKey;
+  end
+  % =======================================================================
+  
+  disp('TODO: add values')
+  %     values.insert(, initialPose);
+  %   values.insert(symbol('v',lastIndex+1), initialVel);
+  
+  %% Update solver
+  % =======================================================================
+  isam.update(factors, values);
+  factors = NonlinearFactorGraph;
+  values = Values;
+  
+  isam.calculateEstimate(currentPoseKey);
+  %   M = isam.marginalCovariance(key_pose);
+  % =======================================================================
+  
+  previousPoseKey = currentPoseKey;
+  previousVelKey = currentVelKey;
+  t_previous = t;
+end
+
+disp('TODO: display results')
+% figure(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')