90 lines
2.9 KiB
Matlab
90 lines
2.9 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GTSAM Copyright 2010, Georgia Tech Research Corporation,
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% Atlanta, Georgia 30332-0415
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% All Rights Reserved
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% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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%
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% See LICENSE for the license information
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%
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% @brief A structure from motion example
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% @author Duy-Nguyen Ta
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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import gtsam.*
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%% Assumptions
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% - Landmarks as 8 vertices of a cube: (10,10,10) (-10,10,10) etc...
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% - Cameras are on a circle around the cube, pointing at the world origin
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% - Each camera sees all landmarks.
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% - Visual measurements as 2D points are given, corrupted by Gaussian noise.
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% Data Options
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options.triangle = false;
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options.nrCameras = 10;
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options.showImages = false;
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%% Generate data
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[data,truth] = VisualISAMGenerateData(options);
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measurementNoiseSigma = 1.0;
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pointNoiseSigma = 0.1;
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poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
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%% Create the graph (defined in visualSLAM.h, derived from NonlinearFactorGraph)
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graph = NonlinearFactorGraph;
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%% Add factors for all measurements
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measurementNoise = noiseModel.Isotropic.Sigma(2,measurementNoiseSigma);
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for i=1:length(data.Z)
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for k=1:length(data.Z{i})
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j = data.J{i}{k};
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graph.add(GenericProjectionFactorCal3_S2(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K));
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end
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end
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%% Add Gaussian priors for a pose and a landmark to constrain the system
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posePriorNoise = noiseModel.Diagonal.Sigmas(poseNoiseSigmas);
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graph.add(PriorFactorPose3(symbol('x',1), truth.cameras{1}.pose, posePriorNoise));
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pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
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graph.add(PriorFactorPoint3(symbol('p',1), truth.points{1}, pointPriorNoise));
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%% Print the graph
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graph.print(sprintf('\nFactor graph:\n'));
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%% Initialize cameras and points close to ground truth in this example
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initialEstimate = Values;
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for i=1:size(truth.cameras,2)
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pose_i = truth.cameras{i}.pose.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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for j=1:size(truth.points,2)
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point_j = Point3(truth.points{j}.vector() + 0.1*randn(3,1));
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initialEstimate.insert(symbol('p',j), point_j);
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end
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initialEstimate.print(sprintf('\nInitial estimate:\n '));
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%% Fine grain optimization, allowing user to iterate step by step
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parameters = LevenbergMarquardtParams;
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parameters.setlambdaInitial(1.0);
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parameters.setVerbosityLM('trylambda');
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optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate, parameters);
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for i=1:5
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optimizer.iterate();
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end
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result = optimizer.values();
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result.print(sprintf('\nFinal result:\n '));
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%% Plot results with covariance ellipses
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marginals = Marginals(graph, result);
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cla
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hold on;
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plot3DPoints(result, [], marginals);
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plot3DTrajectory(result, '*', 1, 8, marginals);
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axis([-40 40 -40 40 -10 20]);axis equal
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view(3)
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colormap('hot')
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