102 lines
3.2 KiB
Matlab
102 lines
3.2 KiB
Matlab
function [pts2dTracksStereo, initialEstimate] = points2DTrackStereo(K, cameraPoses, options, 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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%
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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, 0.05);
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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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points3d = cell(0);
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for i = 1:cylinderNum
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cylinderPointsNum = length(cylinders{i}.Points);
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pointsNum = pointsNum + length(cylinders{i}.Points);
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for j = 1:cylinderPointsNum
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points3d{end+1}.data = cylinders{i}.Points{j};
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points3d{end}.Z = cell(0);
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points3d{end}.cameraConstraint = cell(0);
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points3d{end}.visiblity = false;
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points3d{end}.cov = cell(cameraPosesNum);
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end
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end
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graph.add(PriorFactorPose3(symbol('x', 1), cameraPoses{1}, posePriorNoise));
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%% initialize graph and values
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initialEstimate = Values;
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for i = 1:pointsNum
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point_j = points3d{i}.data.retract(0.05*randn(3,1));
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initialEstimate.insert(symbol('p', i), point_j);
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end
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pts3d = cell(cameraPosesNum, 1);
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cameraPosesCov = cell(cameraPosesNum, 1);
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for i = 1:cameraPosesNum
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pts3d{i} = cylinderSampleProjectionStereo(K, cameraPoses{i}, options.camera.resolution, cylinders);
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if isempty(pts3d{i}.Z)
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continue;
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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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index = pts3d{i}.overallIdx{j};
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points3d{index}.Z{end+1} = pts3d{i}.Z{j};
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points3d{index}.cameraConstraint{end+1} = i;
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points3d{index}.visiblity = true;
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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', index), K));
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end
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pose_i = cameraPoses{i}.retract(poseNoiseSigmas);
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initialEstimate.insert(symbol('x', i), pose_i);
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%% linearize the graph
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marginals = Marginals(graph, initialEstimate);
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for j = 1:pointsNum
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if points3d{j}.visiblity
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points3d{j}.cov{i} = marginals.marginalCovariance(symbol('p', j));
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end
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end
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cameraPosesCov{i} = marginals.marginalCovariance(symbol('x', i));
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end
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%% Plot the result
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plotFlyingResults(points3d, cameraPoses, cameraPosesCov, cylinders, options);
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%% get all the 2d points track information
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pts2dTracksStereo.pt3d = cell(0);
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pts2dTracksStereo.Z = cell(0);
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pts2dTracksStereo.cov = cell(0);
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for i = 1:pointsNum
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if ~points3d{i}.visiblity
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continue;
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end
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pts2dTracksStereo.pt3d{end+1} = points3d{i}.data;
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pts2dTracksStereo.Z{end+1} = points3d{i}.Z;
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pts2dTracksStereo.cov{end+1} = marginals.marginalCovariance(symbol('p', i));
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end
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%
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% %% plot the result with covariance ellipses
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% plotFlyingResults(pts2dTracksStereo.pt3d, pts2dTracksStereo.cov, cameraPoses, cameraPosesCov, cylinders, options);
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end
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