working on imu test

release/4.3a0
Luca 2014-04-10 19:26:53 -04:00
parent c20dd18ab7
commit 24157ca124
1 changed files with 105 additions and 87 deletions

View File

@ -9,91 +9,98 @@ clear all
close all
%% Configuration
useRealData = 0; % controls whether or not to use the Real data (is available) as the ground truth traj
includeIMUFactors = 1; % if true, IMU type 1 Factors will be generated for the random trajectory
includeCameraFactors = 0;
trajectoryLength = 50;
useRealData = 0; % controls whether or not to use the Real data (is available) as the ground truth traj
includeIMUFactors = 1; % if true, IMU type 1 Factors will be generated for the random trajectory
% includeCameraFactors = 0; % not implemented yet
trajectoryLength = 2; % length of the ground truth trajectory
deltaT = 1.0; % amount of time between IMU measurements
vel = [0 0 0]; % initial velocity (used for generating IMU measurements
g = [0; 0; 0]; % gravity
omegaCoriolis = [0; 0; 0]; % Coriolis
% Imu metadata
%% Imu metadata
epsBias = 1e-20;
zeroBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); % bias is not of interest and is set to zero
zeroBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
IMU_metadata.AccelerometerSigma = 1e-5;
IMU_metadata.GyroscopeSigma = 1e-7;
IMU_metadata.IntegrationSigma = 1e-10;
IMU_metadata.BiasAccelerometerSigma = epsBias;
IMU_metadata.BiasGyroscopeSigma = epsBias;
IMU_metadata.BiasAccOmegaInit = epsBias;
noiseVel = noiseModel.Isotropic.Sigma(3, 1e-10);
noiseVel = noiseModel.Isotropic.Sigma(3, 0.1);
noiseBias = noiseModel.Isotropic.Sigma(6, epsBias);
%% Create ground truth trajectory
unsmooth_DP = 0.5; % controls smoothness on translation norm
unsmooth_DR = 0.1; % controls smoothness on rotation norm
gtValues = Values;
gtGraph = NonlinearFactorGraph;
%% Between metadata
if useRealData == 1
sigma_ang = 1e-4;
sigma_cart = 40;
sigma_ang = 1e-4; sigma_cart = 40;
else
sigma_ang = 1e-2;
sigma_cart = 0.1;
sigma_ang = 1e-2; sigma_cart = 0.1;
end
noiseVectorPose = [sigma_ang; sigma_ang; sigma_ang; sigma_cart; sigma_cart; sigma_cart];
noisePose = noiseModel.Diagonal.Sigmas(noiseVectorPose);
if useRealData == 1
%% Create a ground truth trajectory using scenario 2 data
fprintf('\nUsing Scenario 2 ground truth data\n');
% load scenario 2 ground truth data
gtScenario2 = load('truth_scen2.mat', 'Lat', 'Lon', 'Alt', 'Roll', 'Pitch', 'Heading');
% Add first pose
currentPoseKey = symbol('x', 0);
initialPosition = imuSimulator.LatLonHRad_to_ECEF([gtScenario2.Lat(1); gtScenario2.Lon(1); gtScenario2.Alt(1)]);
initialRotation = [gtScenario2.Roll(1); gtScenario2.Pitch(1); gtScenario2.Heading(1)];
currentPose = Pose3.Expmap([initialRotation; initialPosition]); % initial pose
gtValues.insert(currentPoseKey, currentPose);
gtGraph.add(PriorFactorPose3(currentPoseKey, currentPose, noisePose));
prevPose = currentPose;
% Limit the trajectory length
trajectoryLength = min([length(gtScenario2.Lat) trajectoryLength]);
for i=2:trajectoryLength
currentPoseKey = symbol('x', i-1);
gtECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario2.Lat(i); gtScenario2.Lon(i); gtScenario2.Alt(i)]);
gtRotation = [gtScenario2.Roll(i); gtScenario2.Pitch(i); gtScenario2.Heading(i)];
currentPose = Pose3.Expmap([gtRotation; gtECEF]);
% Generate measurements as the current pose measured in the frame of
% the previous pose
deltaPose = prevPose.between(currentPose);
gtDeltaMatrix(i-1,:) = Pose3.Logmap(deltaPose);
prevPose = currentPose;
% Add values
gtValues.insert(currentPoseKey, currentPose);
% Add the factor to the factor graph
gtGraph.add(BetweenFactorPose3(currentPoseKey-1, currentPoseKey, deltaPose, noisePose));
end
else
%% Create a random trajectory as ground truth
fprintf('\nCreating a random ground truth trajectory\n');
% Add priors
currentPoseKey = symbol('x', 0);
currentPose = Pose3; % initial pose
gtValues.insert(currentPoseKey, currentPose);
gtGraph.add(PriorFactorPose3(currentPoseKey, currentPose, noisePose));
%% Create ground truth trajectory
gtValues = Values;
gtGraph = NonlinearFactorGraph;
if useRealData == 1
% % % %% Create a ground truth trajectory from Real data (if available)
% % % fprintf('\nUsing real data as ground truth\n');
% % % gtScenario2 = load('truth_scen2.mat', 'Lat', 'Lon', 'Alt', 'Roll', 'Pitch', 'Heading');
% Time: [4201x1 double]
% Lat: [4201x1 double]
% Lon: [4201x1 double]
% Alt: [4201x1 double]
% VEast: [4201x1 double]
% VNorth: [4201x1 double]
% VUp: [4201x1 double]
% Roll: [4201x1 double]
% Pitch: [4201x1 double]
% Heading
% % %
% % % % Add first pose
% % % currentPoseKey = symbol('x', 0);
% % % initialPosition = imuSimulator.LatLonHRad_to_ECEF([gtScenario2.Lat(1); gtScenario2.Lon(1); gtScenario2.Alt(1)]);
% % % initialRotation = [gtScenario2.Roll(1); gtScenario2.Pitch(1); gtScenario2.Heading(1)];
% % % currentPose = Pose3.Expmap([initialRotation; initialPosition]); % initial pose
% % % gtValues.insert(currentPoseKey, currentPose);
% % % gtGraph.add(PriorFactorPose3(currentPoseKey, currentPose, noisePose));
% % % prevPose = currentPose;
% % %
% % % % Limit the trajectory length
% % % trajectoryLength = min([length(gtScenario2.Lat) trajectoryLength]);
% % %
% % % for i=2:trajectoryLength
% % % currentPoseKey = symbol('x', i-1);
% % % gtECEF = imuSimulator.LatLonHRad_to_ECEF([gtScenario2.Lat(i); gtScenario2.Lon(i); gtScenario2.Alt(i)]);
% % % gtRotation = [gtScenario2.Roll(i); gtScenario2.Pitch(i); gtScenario2.Heading(i)];
% % % currentPose = Pose3.Expmap([gtRotation; gtECEF]);
% % %
% % % % Generate measurements as the current pose measured in the frame of
% % % % the previous pose
% % % deltaPose = prevPose.between(currentPose);
% % % gtDeltaMatrix(i-1,:) = Pose3.Logmap(deltaPose);
% % % prevPose = currentPose;
% % %
% % % % Add values
% % % gtValues.insert(currentPoseKey, currentPose);
% % %
% % % % Add the factor to the factor graph
% % % gtGraph.add(BetweenFactorPose3(currentPoseKey-1, currentPoseKey, deltaPose, noisePose));
% % % end
else
%% Create a random trajectory as ground truth
currentVel = [0 0 0]; % initial velocity (used to generate IMU measurements)
currentPose = Pose3; % initial pose % initial pose
deltaT = 1.0; % amount of time between IMU measurements
g = [0; 0; 0]; % gravity
omegaCoriolis = [0; 0; 0]; % Coriolis
unsmooth_DP = 0.5; % controls smoothness on translation norm
unsmooth_DR = 0.1; % controls smoothness on rotation norm
fprintf('\nCreating a random ground truth trajectory\n');
%% Add priors
currentPoseKey = symbol('x', 0);
gtValues.insert(currentPoseKey, currentPose);
gtGraph.add(PriorFactorPose3(currentPoseKey, currentPose, noisePose));
if includeIMUFactors == 1
currentVelKey = symbol('v', 0);
currentBiasKey = symbol('b', 0);
@ -105,28 +112,31 @@ else
for i=1:trajectoryLength
currentPoseKey = symbol('x', i);
currentVelKey = symbol('v', i);
currentBiasKey = symbol('b', i);
gtDeltaPosition = unsmooth_DP*randn(3,1) + [20;0;0]; % create random vector with mean = [1 0 0] and sigma = 0.5
gtDeltaRotation = unsmooth_DR*randn(3,1) + [0;0;0]; % create random rotation with mean [0 0 0] and sigma = 0.1 (rad)
gtDeltaMatrix(i,:) = [gtDeltaRotation; gtDeltaPosition];
deltaPose = Pose3.Expmap(gtDeltaMatrix(i,:)');
measurements.deltaPose = Pose3.Expmap(gtDeltaMatrix(i,:)');
% "Deduce" ground truth measurements
% deltaPose are the gt measurements - save them in some structure
currentPose = currentPose.compose(deltaPose);
gtValues.insert(currentPoseKey, currentPose);
% Add the factors to the factor graph
% Add the factors to the factor graph
gtGraph.add(BetweenFactorPose3(currentPoseKey-1, currentPoseKey, deltaPose, noisePose));
% Add IMU factors
if includeIMUFactors == 1
currentVelKey = symbol('v', i); % not used if includeIMUFactors is false
currentBiasKey = symbol('b', i); % not used if includeIMUFactors is false
% create accel and gyro measurements based on
gyro = gtDeltaMatrix(i, 1:3)./deltaT;
accel = (gtDeltaMatrix(i, 4:6) - vel.*deltaT).*(2/(deltaT*deltaT));
vel = gtDeltaMatrix(i,4:6)./deltaT;
measurements.imu.gyro = gtDeltaMatrix(i, 1:3)./deltaT;
% acc = (deltaPosition - initialVel * dT) * (2/dt^2)
measurements.imu.accel = (gtDeltaMatrix(i, 4:6) - currentVel.*deltaT).*(2/(deltaT*deltaT));
% update current velocity
currentVel = gtDeltaMatrix(i,4:6)./deltaT;
imuMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ...
zeroBias, ...
IMU_metadata.AccelerometerSigma.^2 * eye(3), ...
@ -147,9 +157,17 @@ else
end
end
gtPoses = Values;
for i=0:trajectoryLength
currentPoseKey = symbol('x', i);
currentPose = gtValues.at(currentPoseKey);
gtPoses.insert(currentPoseKey, currentPose);
end
figure(1)
hold on;
plot3DTrajectory(gtValues, '-r', [], 1, Marginals(gtGraph, gtValues));
plot3DTrajectory(gtPoses, '-r', [], 1, Marginals(gtGraph, gtPoses));
axis equal
numMonteCarloRuns = 100;
@ -186,7 +204,7 @@ for k=1:numMonteCarloRuns
% optimize
optimizer = GaussNewtonOptimizer(graph, gtValues);
estimate = optimizer.optimize();
figure(1)
plot3DTrajectory(estimate, '-b');
@ -208,7 +226,7 @@ for k=1:numMonteCarloRuns
% compute NEES using (estimationError = estimatedValues - gtValues) and estimated covariances
NEES(k,i) = errPosition' * inv(covPosition) * errPosition; % distributed according to a Chi square with n = 3 dof
end
figure(2)
hold on
plot(NEES(k,:),'-b','LineWidth',1.5)
@ -216,7 +234,7 @@ end
%%
ANEES = mean(NEES);
plot(ANEES,'-r','LineWidth',2)
plot(3*ones(size(ANEES,2),1),'k--'); % Expectation(ANEES) = number of dof
plot(3*ones(size(ANEES,2),1),'k--'); % Expectation(ANEES) = number of dof
box on
set(gca,'Fontsize',16)
title('NEES and ANEES');
@ -232,7 +250,7 @@ n = 3; % position vector dimension
N = numMonteCarloRuns; % number of runs
alpha = 0.01; % confidence level
% mean_value = n*N; % mean value of the Chi-square distribution
% mean_value = n*N; % mean value of the Chi-square distribution
% (we divide by n * N and for this reason we expect ANEES around 1)
r1 = chi2inv(alpha, n * N) / (n * N);
r2 = chi2inv(1-alpha, n * N) / (n * N);
@ -252,19 +270,19 @@ set(gca,'Fontsize',16)
title('NEES normalized by dof VS bounds');
%% NEES COMPUTATION (Bar-Shalom 2001, Section 5.4)
% the nees for a single experiment (i) is defined as
% NEES_i = xtilda' * inv(P) * xtilda,
% the nees for a single experiment (i) is defined as
% NEES_i = xtilda' * inv(P) * xtilda,
% where xtilda in R^n is the estimation
% error, and P is the covariance estimated by the approach we want to test
%
%
% Average NEES. Given N Monte Carlo simulations, i=1,...,N, the average
% NEES is:
% ANEES = sum(NEES_i)/N
% The quantity N*ANEES is distributed according to a Chi-square
% distribution with N*n degrees of freedom.
%
% For the single run case, N=1, therefore NEES = ANEES is distributed
% according to a chi-square distribution with n degrees of freedom (e.g. n=3
% For the single run case, N=1, therefore NEES = ANEES is distributed
% according to a chi-square distribution with n degrees of freedom (e.g. n=3
% if we are testing a position estimate)
% Therefore its mean should be n (difficult to see from a single run)
% and, with probability alpha, it should hold: