69 lines
2.5 KiB
Matlab
69 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
|
||
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||
|
|
|
||
|
|
%% 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
|
||
|
|
priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
|
||
|
|
priorNoise = gtsamSharedNoiseModel_Sigmas([0.3; 0.3; 0.1]);
|
||
|
|
graph.addPrior(1, priorMean, priorNoise); % add directly to graph
|
||
|
|
|
||
|
|
%% Add odometry
|
||
|
|
% general noisemodel for odometry
|
||
|
|
odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]);
|
||
|
|
graph.addOdometry(1, 2, gtsamPose2(2.0, 0.0, 0.0 ), odometryNoise);
|
||
|
|
graph.addOdometry(2, 3, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
|
||
|
|
graph.addOdometry(3, 4, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
|
||
|
|
graph.addOdometry(4, 5, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
|
||
|
|
|
||
|
|
%% Add pose constraint
|
||
|
|
model = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]);
|
||
|
|
graph.addConstraint(5, 2, gtsamPose2(2.0, 0.0, pi/2), model);
|
||
|
|
|
||
|
|
% 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, pi/2));
|
||
|
|
initialEstimate.insertPose(4, gtsamPose2(4.0, 2.0, pi ));
|
||
|
|
initialEstimate.insertPose(5, gtsamPose2(2.1, 2.1,-pi/2));
|
||
|
|
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
|
||
|
|
figure(1);clf;
|
||
|
|
plot(result.xs(),result.ys(),'k*-'); hold on
|
||
|
|
plot([result.pose(5).x;result.pose(2).x],[result.pose(5).y;result.pose(2).y],'r-');
|
||
|
|
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 equal
|