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7eb6f0b6b964efade282590ae6f9616041203464 Mon Sep 17 00:00:00 2001 From: TharunSethuraman Date: Wed, 15 Jun 2022 18:33:50 +0530 Subject: [PATCH 2/2] Added visualisation using networkx #22 --- .../script/planerSLAM_for_collected_data.py | 37 ++++++++----------- 1 file changed, 16 insertions(+), 21 deletions(-) diff --git a/gtsam/planar_SLAM/script/planerSLAM_for_collected_data.py b/gtsam/planar_SLAM/script/planerSLAM_for_collected_data.py index 27abf13..9157093 100644 --- a/gtsam/planar_SLAM/script/planerSLAM_for_collected_data.py +++ b/gtsam/planar_SLAM/script/planerSLAM_for_collected_data.py @@ -18,7 +18,8 @@ import numpy as np from gtsam.symbol_shorthand import L, X from sympy import O - +import networkx as nx +from networkx.drawing.nx_pydot import write_dot from typing import Sequence import numpy as np import numpy.linalg as linalg @@ -34,10 +35,8 @@ -# file_name = "/home/ravi/Desktop/Work/semester_2/sdp/Sdp_factor_graphs/gtsam/data_collection/Extracted_data_from_bag_files/sdp_data_collection_look_one_3_marker.csv" -# file_name = '/home/ravi/Desktop/Work/semester_2/sdp/Sdp_factor_graphs/gtsam/data_collection/Extracted_data_from_bag_files/sdp_data_collection_look_one_marker.csv' -# file_name = '/home/ravi/Desktop/Work/semester_2/sdp/Sdp_factor_graphs/gtsam/data_collection/Extracted_data_from_bag_files/sdp_data_collection_look_one_two_marker_full_loop.csv' -file_name = '/home/ravi/Desktop/Work/semester_2/sdp/Sdp_factor_graphs/gtsam/data_collection/Extracted_data_from_bag_files/sdp_data_collection_look_one_two_3_marker.csv' +file_name = "/home/tharun/Desktop/Semester_2/SDP/ss22-factor-graph-slam/gtsam/data_collection/Extracted_data_from_bag_files/sdp_data_collection_look_one_3_marker.csv" + x_distance_step = None y_distance_step = None @@ -179,7 +178,7 @@ def dict_generator(): our_data = data_loader() - our_data = our_data[0::20] # take every 20th data point # to reduce the size of the data + our_data = our_data[0::50] # take every 20th data point # to reduce the size of the data print(our_data) @@ -192,6 +191,7 @@ def dict_generator(): LM_dict ={} cnt=0 + for i in ids_in_data: if (np.isnan(i)): continue @@ -202,12 +202,13 @@ def dict_generator(): return LM_dict, ids_in_data - def main(): """Main runner""" # Getting Landmarks: + G = nx.Graph() + landmarks , ids_in_data = dict_generator() # Create an empty nonlinear factor graph @@ -216,6 +217,7 @@ def main(): # Create the keys corresponding to unknown variables in the factor graph #First node with prior + G.add_edges_from([ ["X0", "L"+str(landmarks[ids_in_data[0]]) ] ]) graph.add(gtsam.PriorFactorPose2(X(0),gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE)) angle = yaw_angle_calculator(0) graph.add(gtsam.BearingRangeFactor2D(X(0), L(landmarks[ids_in_data[0]]), gtsam.Rot2.fromDegrees(angle),0, MEASUREMENT_NOISE)) @@ -226,7 +228,8 @@ def main(): graph.add(gtsam.BetweenFactorPose2(X(i-1), X(i), gtsam.Pose2(0.00944, -0.09073, 0.0),ODOMETRY_NOISE)) angle = yaw_angle_calculator(i) graph.add(gtsam.BearingRangeFactor2D(X(i), L(landmarks[ids_in_data[i]]), gtsam.Rot2.fromDegrees(angle),0, MEASUREMENT_NOISE)) - + G.add_edges_from([ ["X"+str(i-1),"X"+str(i)] ]) + G.add_edges_from([ ["X"+str(i),"L"+str(landmarks[ids_in_data[i]]) ] ]) # Print graph # print("Factor Graph:\n{}".format(graph)) @@ -249,7 +252,6 @@ def main(): # to use, and the amount of information displayed during optimization. # Here we will use the default set of parameters. See the # documentation for the full set of parameters. - params = gtsam.LevenbergMarquardtParams() optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params) @@ -264,20 +266,13 @@ def main(): # final_data.tofile('/home/tharun/Desktop/Semester_2/SDP/GTSAM/gtsam/python/gtsam/examples/csv_files/final_data.csv',sep=',',format='%10.5f') - pd.DataFrame(final_data).to_csv('/home/ravi/Desktop/Work/semester_2/sdp/Sdp_factor_graphs/gtsam/planar_slam_output'+'_look_one_two_3_marker'+'.csv',sep=',',index=False) + pd.DataFrame(final_data).to_csv('/home/tharun/Desktop/Semester_2/SDP/ss22-factor-graph-slam/gtsam/planar_slam_output.csv',sep=',',index=False) print("\nFinal Result:\n{}".format(result)) - # Calculate and print marginal covariances for all variables - # marginals = gtsam.Marginals(graph, result) - - - # for (key, s) in [(X1, "X1"), (X2, "X2"), (X3, "X3"), (L1, "L1"), - # (L2, "L2")]: - # print("{} covariance:\n{}\n".format(s, - # marginals.marginalCovariance(key))) - # graphviz_formatting = gtsam.GraphvizFormatting() - # graph.dot(result,writer=graphviz_formatting) - + print(landmarks) + nx.draw(G, with_labels=True, cmap = plt.get_cmap('jet')) + write_dot(G, '/home/tharun/Desktop/Semester_2/sample.dot') + plt.show() if __name__ == "__main__": main()

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