77 lines
2.5 KiB
Matlab
77 lines
2.5 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 Simple robotics example using the pre-built planar SLAM domain
|
|
% @author Alex Cunningham
|
|
% @author Frank Dellaert
|
|
% @author Chris Beall
|
|
% @author Vadim Indelman
|
|
% @author Can Erdogan
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
%% 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 = pose2SLAMGraph;
|
|
|
|
%% Add prior
|
|
% gaussian for prior
|
|
priorNoise = gtsamSharedNoiseModel_Sigmas([0.3; 0.3; 0.1]);
|
|
priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
|
|
graph.addPrior(1, priorMean, priorNoise); % add directly to graph
|
|
|
|
%% Add odometry
|
|
% general noisemodel for odometry
|
|
odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]);
|
|
odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
|
|
graph.addOdometry(1, 2, odometry, odometryNoise);
|
|
graph.addOdometry(2, 3, odometry, odometryNoise);
|
|
|
|
%% Add measurements
|
|
% general noisemodel for measurements
|
|
meas_model = gtsamSharedNoiseModel_Sigmas([0.1; 0.2]);
|
|
|
|
% print
|
|
graph.print('full graph');
|
|
|
|
%% Initialize to noisy points
|
|
initialEstimate = pose2SLAMValues;
|
|
initialEstimate.insertPose(1, gtsamPose2(0.5, 0.0, 0.2));
|
|
initialEstimate.insertPose(2, gtsamPose2(2.3, 0.1,-0.2));
|
|
initialEstimate.insertPose(3, gtsamPose2(4.1, 0.1, 0.1));
|
|
|
|
initialEstimate.print('initial estimate');
|
|
|
|
%% set up solver, choose ordering and optimize
|
|
%params = gtsamNonlinearOptimizationParameters_newDecreaseThresholds(1e-15, 1e-15);
|
|
%
|
|
%ord = graph.orderingCOLAMD(initialEstimate);
|
|
%
|
|
%result = pose2SLAMOptimizer(graph,initialEstimate,ord,params);
|
|
%result.print('final result');
|
|
|
|
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
|
|
result = graph.optimize(initialEstimate);
|
|
result.print('final result');
|
|
|
|
%% Get the corresponding dense matrix
|
|
ord = graph.orderingCOLAMD(result);
|
|
gfg = graph.linearize(result,ord);
|
|
denseAb = gfg.denseJacobian;
|
|
|
|
%% Get sparse matrix A and RHS b
|
|
IJS = gfg.sparseJacobian_();
|
|
Ab=sparse(IJS(1,:),IJS(2,:),IJS(3,:));
|
|
A = Ab(:,1:end-1);
|
|
b = full(Ab(:,end));
|
|
spy(A);
|