110 lines
3.7 KiB
Matlab
110 lines
3.7 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 simple visual SLAM example for structure from motion
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% @author Duy-Nguyen Ta
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Create a triangle target, just 3 points on a plane
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nPoints = 3;
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r = 10;
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points = {};
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for j=1:nPoints
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theta = (j-1)*2*pi/nPoints;
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points{j} = gtsamPoint3([r*cos(theta), r*sin(theta), 0]');
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end
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%% Create camera cameras on a circle around the triangle
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nCameras = 10;
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height = 10;
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r = 30;
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cameras = {};
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K = gtsamCal3_S2(500,500,0,640/2,480/2);
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for i=1:nCameras
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theta = (i-1)*2*pi/nCameras;
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t = gtsamPoint3([r*cos(theta), r*sin(theta), height]');
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cameras{i} = gtsamSimpleCamera_lookat(t, gtsamPoint3, gtsamPoint3([0,0,1]'), K);
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end
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odometry = cameras{1}.pose.between(cameras{2}.pose);
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poseNoise = gtsamSharedNoiseModel_Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
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pointNoise = gtsamSharedNoiseModel_Sigma(3, 0.1);
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measurementNoise = gtsamSharedNoiseModel_Sigma(2, 1.0);
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%% Create an ISAM object for inference
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isam = visualSLAMISAM;
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%% Update ISAM
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newFactors = visualSLAMGraph;
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initialEstimates = visualSLAMValues;
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for i=1:nCameras
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% Prior for the first pose or odometry for subsequent cameras
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if (i==1)
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newFactors.addPosePrior(symbol('x',1), cameras{1}.pose, poseNoise);
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for j=1:nPoints
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newFactors.addPointPrior(symbol('l',j), points{j}, pointNoise);
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end
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else
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newFactors.addOdometry(symbol('x',i-1), symbol('x',i), odometry, poseNoise);
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end
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% Visual measurement factors
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for j=1:nPoints
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zij = cameras{i}.project(points{j});
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newFactors.addMeasurement(zij, measurementNoise, symbol('x',i), symbol('l',j), K);
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end
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% Initial estimates for the new pose. Also initialize points while in
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% the first frame.
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if (i==1)
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initialEstimates.insertPose(symbol('x',i), cameras{i}.pose);
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for j=1:size(points,2)
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initialEstimates.insertPoint(symbol('l',j), points{j});
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end
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else
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%TODO: this might be suboptimal since "result" is not the fully
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%optimized result
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if (i==2), prevPose = cameras{1}.pose;
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else, prevPose = result.pose(symbol('x',i-1)); end
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initialEstimates.insertPose(symbol('x',i), prevPose.compose(odometry));
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end
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% Update ISAM, only update for the second frame onward
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% Update the first frame will cause error, since it's under constrained
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if (i>=2)
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isam.update(newFactors, initialEstimates);
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emptyFactors = visualSLAMGraph;
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emptyEstimates = visualSLAMValues;
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result = isam.estimate();
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% Plot first result
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h=figure(1);clf
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hold on;
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for j=1:size(points,2)
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P = isam.marginalCovariance(symbol('l',j));
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point_j = result.point(symbol('l',j));
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plot3(point_j.x, point_j.y, point_j.z,'marker','o');
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covarianceEllipse3D([point_j.x;point_j.y;point_j.z],P);
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end
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for ii=1:i
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P = isam.marginalCovariance(symbol('x',ii));
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pose_ii = result.pose(symbol('x',ii));
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plotPose3(pose_ii,P,10);
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end
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axis([-50 50 -50 50 -50 50])
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colormap('hot')
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%print(h,'-dpng',sprintf('VisualISAM_%03d.png',i));
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% Reset newFactors and initialEstimates to prepare for the next
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% update
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newFactors = visualSLAMGraph;
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initialEstimates = visualSLAMValues;
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end
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end |