59 lines
2.2 KiB
Matlab
59 lines
2.2 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 Example of a simple 2D localization example
|
|
% @author Frank Dellaert
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
%% Assumptions
|
|
% - Robot poses are facing along the X axis (horizontal, to the right in 2D)
|
|
% - The robot moves 2 meters each step
|
|
% - The robot is on a grid, moving 2 meters each step
|
|
|
|
%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
|
|
graph = pose2SLAMGraph;
|
|
|
|
%% Add two odometry factors
|
|
odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
|
|
odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
|
|
graph.addOdometry(1, 2, odometry, odometryNoise);
|
|
graph.addOdometry(2, 3, odometry, odometryNoise);
|
|
|
|
%% Add three "GPS" measurements
|
|
% We use Pose2 Priors here with high variance on theta
|
|
noiseModel = gtsamSharedNoiseModel_Sigmas([0.1; 0.1; 10]);
|
|
graph.addPrior(1, gtsamPose2(0.0, 0.0, 0.0), noiseModel);
|
|
graph.addPrior(2, gtsamPose2(2.0, 0.0, 0.0), noiseModel);
|
|
graph.addPrior(3, gtsamPose2(4.0, 0.0, 0.0), noiseModel);
|
|
|
|
%% print
|
|
graph.print(sprintf('\nFactor graph:\n'));
|
|
|
|
%% 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(sprintf('\nInitial estimate:\n '));
|
|
|
|
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
|
|
result = graph.optimize(initialEstimate);
|
|
result.print(sprintf('\nFinal result:\n '));
|
|
|
|
%% Plot Covariance Ellipses
|
|
cla;
|
|
plot(result.xs(),result.ys(),'k*-'); hold on
|
|
marginals = graph.marginals(result);
|
|
for i=1:result.size()
|
|
pose_i = result.pose(i);
|
|
P{i}=marginals.marginalCovariance(i);
|
|
plotPose2(pose_i,'g',P{i})
|
|
end
|
|
axis([-0.6 4.8 -1 1])
|
|
axis equal
|