Got rid of redundant examples (clutter!)

release/4.3a0
Frank Dellaert 2012-06-13 12:06:02 +00:00
parent 991d8f3c5f
commit 25b4a15e94
2 changed files with 0 additions and 135 deletions

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GTSAM Copyright 3510, Georgia Tech Research Corporation,
% Atlanta, Georgia 30332-0415
% All Rights Reserved
% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
%
% See LICENSE for the license information
%
% @brief A simple visual SLAM example for structure from motion
% @author Duy-Nguyen Ta
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Data Options
options.triangle = true;
options.nrCameras = 10;
options.showImages = false;
% iSAM Options
options.hardConstraint = false;
options.pointPriors = false;
options.batchInitialization = true;
options.reorderInterval = 10;
options.alwaysRelinearize = false;
% Display Options
options.saveDotFile = false;
options.printStats = false;
options.drawInterval = 5;
options.cameraInterval = 1;
options.drawTruePoses = false;
options.saveFigures = false;
options.saveDotFiles = false;
%% Generate data
[data,truth] = VisualISAMGenerateData(options);
%% Initialize iSAM with the first pose and points
[noiseModels,isam,result] = VisualISAMInitialize(data,truth,options);
figure(1);
VisualISAMPlot(truth, data, isam, result, options)
%% Main loop for iSAM: stepping through all poses
for frame_i=3:options.nrCameras
[isam,result] = VisualISAMStep(data,noiseModels,isam,result,options);
if mod(frame_i,options.drawInterval)==0
VisualISAMPlot(truth, data, isam, result, options)
end
end

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GTSAM Copyright 2010, Georgia Tech Research Corporation,
% Atlanta, Georgia 30332-0415
% All Rights Reserved
% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
%
% See LICENSE for the license information
%
% @brief A simple visual SLAM example for structure from motion
% @author Duy-Nguyen Ta
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Create a triangle target, just 3 points on a plane
r = 10;
points = {};
for j=1:3
theta = (j-1)*2*pi/3;
points{j} = gtsamPoint3([r*cos(theta), r*sin(theta), 0]');
end
%% Create camera cameras on a circle around the triangle
nCameras = 6;
height = 10;
r = 30;
cameras = {};
K = gtsamCal3_S2(500,500,0,640/2,480/2);
for i=1:nCameras
theta = (i-1)*2*pi/nCameras;
t = gtsamPoint3([r*cos(theta), r*sin(theta), height]');
cameras{i} = gtsamSimpleCamera_lookat(t, gtsamPoint3, gtsamPoint3([0,0,1]'), K)
end
%% Create the graph (defined in visualSLAM.h, derived from NonlinearFactorGraph)
graph = visualSLAMGraph;
%% Add factors for all measurements
measurementNoiseSigma=1; % in pixels
measurementNoise = gtsamSharedNoiseModel_Sigma(2,measurementNoiseSigma);
for i=1:nCameras
for j=1:3
zij = cameras{i}.project(points{j}); % you can add noise here if desired
graph.addMeasurement(zij, measurementNoise, symbol('x',i), symbol('l',j), K);
end
end
%% Add Gaussian priors for 3 points to constrain the system
pointPriorNoise = gtsamSharedNoiseModel_Sigma(3,0.1);
for j=1:3
graph.addPointPrior(symbol('l',j), points{j}, pointPriorNoise);
end
%% Print the graph
graph.print(sprintf('\nFactor graph:\n'));
%% Initialize to noisy cameras and points
initialEstimate = visualSLAMValues;
for i=1:size(cameras,2)
initialEstimate.insertPose(symbol('x',i), cameras{i}.pose);
end
for j=1:size(points,2)
initialEstimate.insertPoint(symbol('l',j), points{j});
end
initialEstimate.print(sprintf('\nInitial estimate:\n '));
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
result = graph.optimize(initialEstimate);
result.print(sprintf('\nFinal result:\n '));
%% Plot results with covariance ellipses
marginals = graph.marginals(result);
figure(1);clf
hold on;
for j=1:size(points,2)
P = marginals.marginalCovariance(symbol('l',j));
point_j = result.point(symbol('l',j));
plot3(point_j.x, point_j.y, point_j.z,'marker','o');
covarianceEllipse3D([point_j.x;point_j.y;point_j.z],P);
end
for i=1:size(cameras,2)
P = marginals.marginalCovariance(symbol('x',i))
pose_i = result.pose(symbol('x',i))
plotPose3(pose_i,P,10);
end
axis([-20 20 -20 20 -1 15]);
axis equal
view(-37,40)