gtsam/matlab/gtsam_examples/Pose2SLAMwSPCG.m

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% 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 Simple 2D robotics example using the SimpleSPCGSolver
% @author Yong-Dian Jian
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import gtsam.*

%% Assumptions
%  - All values are axis aligned
%  - Robot poses are facing along the X axis (horizontal, to the right in images)
%  - We have full odometry for measurements
%  - The robot is on a grid, moving 2 meters each step

%% Create graph container and add factors to it
graph = NonlinearFactorGraph;

%% Add prior
% gaussian for prior
priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
graph.add(PriorFactorPose2(1, priorMean, priorNoise)); % add directly to graph

%% Add odometry
% general noisemodel for odometry
odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
graph.add(BetweenFactorPose2(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise));
graph.add(BetweenFactorPose2(2, 3, Pose2(2.0, 0.0, pi/2), odometryNoise));
graph.add(BetweenFactorPose2(3, 4, Pose2(2.0, 0.0, pi/2), odometryNoise));
graph.add(BetweenFactorPose2(4, 5, Pose2(2.0, 0.0, pi/2), odometryNoise));

%% Add pose constraint
model = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
graph.add(BetweenFactorPose2(5, 2, Pose2(2.0, 0.0, pi/2), model));

% print
graph.print(sprintf('\nFactor graph:\n'));

%% Initialize to noisy points
initialEstimate = Values;
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2 ));
initialEstimate.insert(2, Pose2(2.3, 0.1,-0.2 ));
initialEstimate.insert(3, Pose2(4.1, 0.1, pi/2));
initialEstimate.insert(4, Pose2(4.0, 2.0, pi  ));
initialEstimate.insert(5, Pose2(2.1, 2.1,-pi/2));
initialEstimate.print(sprintf('\nInitial estimate:\n'));

%% Optimize using Levenberg-Marquardt optimization with SubgraphSolver
params = gtsam.LevenbergMarquardtParams;
subgraphParams = gtsam.SubgraphSolverParameters;
params.setLinearSolverType('CONJUGATE_GRADIENT');
params.setIterativeParams(subgraphParams);
optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate);
result = optimizer.optimize();
result.print(sprintf('\nFinal result:\n'));