74 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Matlab
		
	
	
		
		
			
		
	
	
			74 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Matlab
		
	
	
| 
								 | 
							
								%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
							 | 
						||
| 
								 | 
							
								% GTSAM Copyright 2010, Georgia Tech Research Corporation, 
							 | 
						||
| 
								 | 
							
								% Atlanta, Georgia 30332-0415
							 | 
						||
| 
								 | 
							
								% All Rights Reserved
							 | 
						||
| 
								 | 
							
								% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
							 | 
						||
| 
								 | 
							
								% 
							 | 
						||
| 
								 | 
							
								% See LICENSE for the license information
							 | 
						||
| 
								 | 
							
								%
							 | 
						||
| 
								 | 
							
								% @brief A structure from motion example
							 | 
						||
| 
								 | 
							
								% @author Duy-Nguyen Ta
							 | 
						||
| 
								 | 
							
								%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								import gtsam.*
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								options.triangle = false;
							 | 
						||
| 
								 | 
							
								options.nrCameras = 10;
							 | 
						||
| 
								 | 
							
								options.showImages = false;
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								[data,truth] = VisualISAMGenerateData(options);
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								measurementNoiseSigma = 1.0;
							 | 
						||
| 
								 | 
							
								pointNoiseSigma = 0.1;
							 | 
						||
| 
								 | 
							
								poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								graph = NonlinearFactorGraph;
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								%% Add factors for all measurements
							 | 
						||
| 
								 | 
							
								measurementNoise = noiseModel.Isotropic.Sigma(2,measurementNoiseSigma);
							 | 
						||
| 
								 | 
							
								for i=1:length(data.Z)
							 | 
						||
| 
								 | 
							
								    for k=1:length(data.Z{i})
							 | 
						||
| 
								 | 
							
								        j = data.J{i}{k};
							 | 
						||
| 
								 | 
							
								        graph.add(GenericProjectionFactorCal3_S2(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K));
							 | 
						||
| 
								 | 
							
								    end
							 | 
						||
| 
								 | 
							
								end
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								posePriorNoise  = noiseModel.Diagonal.Sigmas(poseNoiseSigmas);
							 | 
						||
| 
								 | 
							
								graph.add(PriorFactorPose3(symbol('x',1), truth.cameras{1}.pose, posePriorNoise));
							 | 
						||
| 
								 | 
							
								pointPriorNoise  = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
							 | 
						||
| 
								 | 
							
								graph.add(PriorFactorPoint3(symbol('p',1), truth.points{1}, pointPriorNoise));
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								%% Initial estimate
							 | 
						||
| 
								 | 
							
								initialEstimate = Values;
							 | 
						||
| 
								 | 
							
								for i=1:size(truth.cameras,2)
							 | 
						||
| 
								 | 
							
								    pose_i = truth.cameras{i}.pose;
							 | 
						||
| 
								 | 
							
								    initialEstimate.insert(symbol('x',i), pose_i);
							 | 
						||
| 
								 | 
							
								end
							 | 
						||
| 
								 | 
							
								for j=1:size(truth.points,2)
							 | 
						||
| 
								 | 
							
								    point_j = truth.points{j};
							 | 
						||
| 
								 | 
							
								    initialEstimate.insert(symbol('p',j), point_j);
							 | 
						||
| 
								 | 
							
								end
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								%% Optimization
							 | 
						||
| 
								 | 
							
								optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
							 | 
						||
| 
								 | 
							
								for i=1:5
							 | 
						||
| 
								 | 
							
								    optimizer.iterate();
							 | 
						||
| 
								 | 
							
								end
							 | 
						||
| 
								 | 
							
								result = optimizer.values();
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								%% Marginalization
							 | 
						||
| 
								 | 
							
								marginals = Marginals(graph, result);
							 | 
						||
| 
								 | 
							
								marginals.marginalCovariance(symbol('p',1));
							 | 
						||
| 
								 | 
							
								marginals.marginalCovariance(symbol('x',1));
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								%% Check optimized results, should be equal to ground truth
							 | 
						||
| 
								 | 
							
								for i=1:size(truth.cameras,2)
							 | 
						||
| 
								 | 
							
								    pose_i = result.at(symbol('x',i));
							 | 
						||
| 
								 | 
							
								    CHECK('pose_i.equals(truth.cameras{i}.pose,1e-5)',pose_i.equals(truth.cameras{i}.pose,1e-5))
							 | 
						||
| 
								 | 
							
								end
							 | 
						||
| 
								 | 
							
								
							 | 
						||
| 
								 | 
							
								for j=1:size(truth.points,2)
							 | 
						||
| 
								 | 
							
								    point_j = result.at(symbol('p',j));
							 | 
						||
| 
								 | 
							
								    CHECK('point_j.equals(truth.points{j},1e-5)',point_j.equals(truth.points{j},1e-5))
							 | 
						||
| 
								 | 
							
								end
							 |