78 lines
2.8 KiB
Matlab
78 lines
2.8 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GTSAM Copyright 2010, Georgia Tech Research Corporation,
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% Atlanta, Georgia 30332-0415
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% All Rights Reserved
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% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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%
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% See LICENSE for the license information
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%
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% @brief Checks for serialization using basic string interface
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% @author Alex Cunningham
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% @author Frank Dellaert
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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import gtsam.*
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%% Create keys for variables
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i1 = symbol('x',1); i2 = symbol('x',2); i3 = symbol('x',3);
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j1 = symbol('l',1); j2 = symbol('l',2);
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%% Create values and verify string serialization
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pose1=Pose2(0.5, 0.0, 0.2);
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pose2=Pose2(2.3, 0.1,-0.2);
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pose3=Pose2(4.1, 0.1, 0.1);
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landmark1=Point2(1.8, 2.1);
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landmark2=Point2(4.1, 1.8);
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serialized_pose1 = pose1.string_serialize();
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pose1ds = Pose2.string_deserialize(serialized_pose1);
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CHECK('pose1ds.equals(pose1, 1e-9)', pose1ds.equals(pose1, 1e-9));
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%% Create and serialize Values
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values = Values;
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values.insert(i1, pose1);
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values.insert(i2, pose2);
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values.insert(i3, pose3);
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values.insert(j1, landmark1);
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values.insert(j2, landmark2);
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serialized_values = values.string_serialize();
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valuesds = Values.string_deserialize(serialized_values);
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CHECK('valuesds.equals(values, 1e-9)', valuesds.equals(values, 1e-9));
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%% Create graph and factors and serialize
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graph = NonlinearFactorGraph;
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% Prior factor
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priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
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priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
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graph.add(PriorFactorPose2(i1, priorMean, priorNoise)); % add directly to graph
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% Between Factors
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odometry = Pose2(2.0, 0.0, 0.0);
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odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.add(BetweenFactorPose2(i1, i2, odometry, odometryNoise));
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graph.add(BetweenFactorPose2(i2, i3, odometry, odometryNoise));
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% Range Factors
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rNoise = noiseModel.Diagonal.Sigmas([0.2]);
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graph.add(RangeFactor2D(i1, j1, sqrt(4+4), rNoise));
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graph.add(RangeFactor2D(i2, j1, 2, rNoise));
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graph.add(RangeFactor2D(i3, j2, 2, rNoise));
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% Bearing Factors
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degrees = pi/180;
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bNoise = noiseModel.Diagonal.Sigmas([0.1]);
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graph.add(BearingFactor2D(i1, j1, Rot2(45*degrees), bNoise));
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graph.add(BearingFactor2D(i2, j1, Rot2(90*degrees), bNoise));
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graph.add(BearingFactor2D(i3, j2, Rot2(90*degrees), bNoise));
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% BearingRange Factors
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brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
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graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(4+4), brNoise));
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graph.add(BearingRangeFactor2D(i2, j1, Rot2(90*degrees), 2, brNoise));
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graph.add(BearingRangeFactor2D(i3, j2, Rot2(90*degrees), 2, brNoise));
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serialized_graph = graph.string_serialize();
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graphds = NonlinearFactorGraph.string_deserialize(serialized_graph);
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CHECK('graphds.equals(graph, 1e-9)', graphds.equals(graph, 1e-9)); |