86 lines
2.8 KiB
Matlab
86 lines
2.8 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 Basic VO Example with 3 landmarks and two cameras
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% @author Chris Beall
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Assumptions
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% - For simplicity this example is in the camera's coordinate frame
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% - X: right, Y: down, Z: forward
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% - Pose x1 is at the origin, Pose 2 is 1 meter forward (along Z-axis)
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% - x1 is fixed with a constraint, x2 is initialized with noisy values
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% - No noise on measurements
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%% Create keys for variables
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x1 = symbol('x',1); x2 = symbol('x',2);
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l1 = symbol('l',1); l2 = symbol('l',2); l3 = symbol('l',3);
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%% Create graph container and add factors to it
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graph = visualSLAM.Graph;
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%% add a constraint on the starting pose
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import gtsam.*
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first_pose = Pose3();
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graph.addPoseConstraint(x1, first_pose);
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%% Create realistic calibration and measurement noise model
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% format: fx fy skew cx cy baseline
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import gtsam.*
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K = Cal3_S2Stereo(1000, 1000, 0, 320, 240, 0.2);
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stereo_model = noiseModel.Diagonal.Sigmas([1.0; 1.0; 1.0]);
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%% Add measurements
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import gtsam.*
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% pose 1
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graph.addStereoMeasurement(StereoPoint2(520, 480, 440), stereo_model, x1, l1, K);
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graph.addStereoMeasurement(StereoPoint2(120, 80, 440), stereo_model, x1, l2, K);
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graph.addStereoMeasurement(StereoPoint2(320, 280, 140), stereo_model, x1, l3, K);
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%pose 2
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graph.addStereoMeasurement(StereoPoint2(570, 520, 490), stereo_model, x2, l1, K);
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graph.addStereoMeasurement(StereoPoint2( 70, 20, 490), stereo_model, x2, l2, K);
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graph.addStereoMeasurement(StereoPoint2(320, 270, 115), stereo_model, x2, l3, K);
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%% Create initial estimate for camera poses and landmarks
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import gtsam.*
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initialEstimate = visualSLAM.Values;
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initialEstimate.insertPose(x1, first_pose);
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% noisy estimate for pose 2
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initialEstimate.insertPose(x2, Pose3(Rot3(), Point3(0.1,-.1,1.1)));
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initialEstimate.insertPoint(l1, Point3( 1, 1, 5));
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initialEstimate.insertPoint(l2, Point3(-1, 1, 5));
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initialEstimate.insertPoint(l3, Point3( 0,-.5, 5));
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%% optimize
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fprintf(1,'Optimizing\n'); tic
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result = graph.optimize(initialEstimate,1);
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toc
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%% visualize initial trajectory, final trajectory, and final points
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cla; hold on;
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axis normal
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axis([-1 6 -2 2 -1.5 1.5]);
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axis equal
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view(-38,12)
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% initial trajectory in red (rotated so Z is up)
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T=initialEstimate.translations;
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plot3(T(:,3),-T(:,1),-T(:,2), '-*r','LineWidth',2);
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% final trajectory in green (rotated so Z is up)
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T=result.translations;
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plot3(T(:,3),-T(:,1),-T(:,2), '-*g','LineWidth',2);
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xlabel('X (m)'); ylabel('Y (m)'); zlabel('Z (m)');
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% optimized 3D points (rotated so Z is up)
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P = result.points();
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plot3(P(:,3),-P(:,1),-P(:,2),'*');
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