85 lines
2.8 KiB
Matlab
85 lines
2.8 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 An SFM example (adapted from SFMExample.m) optimizing calibration
|
|
% @author Yong-Dian Jian
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
import gtsam.*
|
|
|
|
%% Assumptions
|
|
% - Landmarks as 8 vertices of a cube: (10,10,10) (-10,10,10) etc...
|
|
% - Cameras are on a circle around the cube, pointing at the world origin
|
|
% - Each camera sees all landmarks.
|
|
% - Visual measurements as 2D points are given, corrupted by Gaussian noise.
|
|
|
|
% Data Options
|
|
options.triangle = false;
|
|
options.nrCameras = 10;
|
|
options.showImages = false;
|
|
|
|
%% Generate data
|
|
[data,truth] = VisualISAMGenerateData(options);
|
|
|
|
measurementNoiseSigma = 1.0;
|
|
pointNoiseSigma = 0.1;
|
|
cameraNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1 ...
|
|
0.001*ones(1,5)]';
|
|
|
|
%% Create the graph (defined in visualSLAM.h, derived from NonlinearFactorGraph)
|
|
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(GeneralSFMFactorCal3_S2(data.Z{i}{k}, measurementNoise, symbol('c',i), symbol('p',j)));
|
|
end
|
|
end
|
|
|
|
%% Add Gaussian priors for a pose and a landmark to constrain the system
|
|
cameraPriorNoise = noiseModel.Diagonal.Sigmas(cameraNoiseSigmas);
|
|
firstCamera = PinholeCameraCal3_S2(truth.cameras{1}.pose, truth.K);
|
|
graph.add(PriorFactorSimpleCamera(symbol('c',1), firstCamera, cameraPriorNoise));
|
|
|
|
pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
|
|
graph.add(PriorFactorPoint3(symbol('p',1), truth.points{1}, pointPriorNoise));
|
|
|
|
%% Print the graph
|
|
graph.print(sprintf('\nFactor graph:\n'));
|
|
|
|
|
|
%% Initialize cameras and points close to ground truth in this example
|
|
initialEstimate = Values;
|
|
for i=1:size(truth.cameras,2)
|
|
pose_i = truth.cameras{i}.pose.retract(0.1*randn(6,1));
|
|
camera_i = PinholeCameraCal3_S2(pose_i, truth.K);
|
|
initialEstimate.insert(symbol('c',i), camera_i);
|
|
end
|
|
for j=1:size(truth.points,2)
|
|
point_j = Point3(truth.points{j}.vector() + 0.1*randn(3,1));
|
|
initialEstimate.insert(symbol('p',j), point_j);
|
|
end
|
|
initialEstimate.print(sprintf('\nInitial estimate:\n '));
|
|
|
|
%% Fine grain optimization, allowing user to iterate step by step
|
|
parameters = LevenbergMarquardtParams;
|
|
parameters.setlambdaInitial(1.0);
|
|
parameters.setVerbosityLM('trylambda');
|
|
|
|
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate, parameters);
|
|
|
|
for i=1:5
|
|
optimizer.iterate();
|
|
end
|
|
|
|
result = optimizer.values();
|
|
result.print(sprintf('\nFinal result:\n '));
|