72 lines
2.3 KiB
Matlab
72 lines
2.3 KiB
Matlab
function pts2dTracksStereo = points2DTrackStereo(K, cameraPoses, imageSize, cylinders)
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% Assess how accurately we can reconstruct points from a particular monocular camera setup.
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% After creation of the factor graph for each track, linearize it around ground truth.
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% There is no optimization
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% @author: Zhaoyang Lv
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import gtsam.*
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%% create graph
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graph = NonlinearFactorGraph;
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%% create the noise factors
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poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
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posePriorNoise = noiseModel.Diagonal.Sigmas(poseNoiseSigmas);
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stereoNoise = noiseModel.Isotropic.Sigma(3,1);
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cameraPosesNum = length(cameraPoses);
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%% add measurements and initial camera & points values
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pointsNum = 0;
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cylinderNum = length(cylinders);
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for i = 1:cylinderNum
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pointsNum = pointsNum + length(cylinders{i}.Points);
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end
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pts3d = cell(cameraPosesNum, 1);
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initialEstimate = Values;
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initialized = false;
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for i = 1:cameraPosesNum
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pts3d{i} = cylinderSampleProjectionStereo(K, cameraPoses{i}, imageSize, cylinders);
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if ~initialized
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graph.add(PriorFactorPose3(symbol('x', 1), cameraPoses{i}, posePriorNoise));
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initialized = true;
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end
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measurementNum = length(pts3d{i}.Z);
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for j = 1:measurementNum
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graph.add(GenericStereoFactor3D(StereoPoint2(pts3d{i}.Z{j}.uL, pts3d{i}.Z{j}.uR, pts3d{i}.Z{j}.v), ...
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stereoNoise, symbol('x', i), symbol('p', pts3d{i}.overallIdx{j}), K));
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end
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end
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%% initialize cameras and points close to ground truth
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for i = 1:cameraPosesNum
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pose_i = cameraPoses{i}.retract(0.1*randn(6,1));
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initialEstimate.insert(symbol('x', i), pose_i);
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end
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ptsIdx = 0;
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for i = 1:length(cylinders)
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for j = 1:length(cylinders{i}.Points)
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ptsIdx = ptsIdx + 1;
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point_j = cylinders{i}.Points{j}.retract(0.1*randn(3,1));
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initialEstimate.insert(symbol('p', ptsIdx), point_j);
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end
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end
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%% Print the graph
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graph.print(sprintf('\nFactor graph:\n'));
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marginals = Marginals(graph, initialEstimate);
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%% get all the 2d points track information
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% currently throws the Indeterminant linear system exception
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for k = 1:cameraPosesNum
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pts2dTracksStereo.pt3d{ptx} = pts3d{k}.data{idx};
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pts2dTracksStereo.Z{ptx} = pts3d{k}.Z{idx};
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pts2dTracksStereo.cov{ptx} = marginals.marginalCovariance(symbol('p',pts3d{k}.overallIdx{visiblePointIdx}));
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end
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end
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