101 lines
3.4 KiB
Matlab
101 lines
3.4 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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%% 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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%% Generate simulated data
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% 3D landmarks as vertices of a cube
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points = {gtsamPoint3([10 10 10]'),...
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gtsamPoint3([-10 10 10]'),...
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gtsamPoint3([-10 -10 10]'),...
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gtsamPoint3([10 -10 10]'),...
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gtsamPoint3([10 10 -10]'),...
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gtsamPoint3([-10 10 -10]'),...
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gtsamPoint3([-10 -10 -10]'),...
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gtsamPoint3([10 -10 -10]')};
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% Camera poses on a circle around the cube, pointing at the world origin
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nCameras = 6;
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height = 0;
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r = 30;
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poses = {};
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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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camera = gtsamSimpleCamera_lookat(t, gtsamPoint3(), gtsamPoint3([0,0,1]'), gtsamCal3_S2())
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poses{i} = camera.pose();
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end
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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 = visualSLAMGraph;
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%% Add factors for all measurements
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measurementNoise = gtsamSharedNoiseModel_Sigma(2,measurementNoiseSigma);
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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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camera = gtsamSimpleCamera(K,poses{i});
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for j=1:size(points,2)
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zij = camera.project(points{j}); % you can add noise here if desired
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graph.addMeasurement(zij, measurementNoise, symbol('x',i), symbol('l',j), 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 = gtsamSharedNoiseModel_Sigmas(poseNoiseSigmas);
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graph.addPosePrior(symbol('x',1), poses{1}, posePriorNoise);
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pointPriorNoise = gtsamSharedNoiseModel_Sigma(3,pointNoiseSigma);
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graph.addPointPrior(symbol('l',1), 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 to noisy poses and points
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initialEstimate = visualSLAMValues;
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for i=1:size(poses,2)
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initialEstimate.insertPose(symbol('x',i), poses{i});
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end
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for j=1:size(points,2)
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initialEstimate.insertPoint(symbol('l',j), points{j});
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end
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initialEstimate.print(sprintf('\nInitial estimate:\n '));
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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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result = graph.optimize(initialEstimate);
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result.print(sprintf('\nFinal result:\n '));
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%% Plot results with covariance ellipses
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marginals = graph.marginals(result);
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figure(1);clf
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hold on;
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for j=1:size(points,2)
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P = marginals.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 i=1:size(poses,2)
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P = marginals.marginalCovariance(symbol('x',i))
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pose_i = result.pose(symbol('x',i))
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plotPose3(pose_i,P,10);
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end
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axis equal
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