57 lines
1.7 KiB
Matlab
57 lines
1.7 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GTSAM Copyright 2010-2013, 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 Monocular VO Example with 5 landmarks and two cameras
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% @author Frank Dellaert
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% import
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import gtsam.*
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%% Create two cameras and corresponding essential matrix E
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aRb = Rot3.Yaw(pi/2);
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aTb = Point3 (0.1, 0, 0);
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identity=Pose3;
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aPb = Pose3(aRb, aTb);
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cameraA = CalibratedCamera(identity);
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cameraB = CalibratedCamera(aPb);
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%% Create 5 points
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P = { Point3(0, 0, 1), Point3(-0.1, 0, 1), Point3(0.1, 0, 1), Point3(0, 0.5, 0.5), Point3(0, -0.5, 0.5) };
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%% Project points in both cameras
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for i=1:5
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pA{i}= cameraA.project(P{i});
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pB{i}= cameraB.project(P{i});
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end
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%% We start with a factor graph and add epipolar constraints to it
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% Noise sigma is 1cm, assuming metric measurements
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graph = NonlinearFactorGraph;
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model = noiseModel.Isotropic.Sigma(1,0.01);
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for i=1:5
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graph.add(EssentialMatrixFactor(1, pA{i}, pB{i}, model));
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end
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%% Create initial estimate
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initial = Values;
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trueE = EssentialMatrix(aRb,Unit3(aTb));
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initialE = trueE.retract([0.1, -0.1, 0.1, 0.1, -0.1]');
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initial.insert(1, initialE);
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%% Optimize
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parameters = LevenbergMarquardtParams;
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% parameters.setVerbosity('ERROR');
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optimizer = LevenbergMarquardtOptimizer(graph, initial, parameters);
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result = optimizer.optimize();
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%% Print result (as essentialMatrix) and error
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error = graph.error(result)
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E = result.atEssentialMatrix(1)
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