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| def | __init__ (self, str filename, float marginal_threshold=0.9999, int max_loop_count=150, int update_frequency=3, int max_num_hypotheses=10, int relinearization_frequency=10, bool plot_hypotheses=False) |
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| def | hybrid_loop_closure_factor (self, loop_counter, key_s, key_t, Pose2 measurement) |
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| HybridNonlinearFactor | hybrid_odometry_factor (self, key_s, key_t, m, pose_array) |
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| def | plot_all_hypotheses (self, discrete_keys, num_poses, num_iters=0) |
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| float | reinitialize (self) |
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| def | run (self) |
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| def | save_results (self, result, final_key, time_list) |
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| float | smoother_update (self, max_num_hypotheses) |
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| def | write_result (self, result, num_poses, filename="Hybrid_city10000.txt") |
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| def | write_timing_info (self, time_list, time_filename="Hybrid_City10000_time.txt") |
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Experiment Class
Definition at line 178 of file HybridCity10000.py.
◆ __init__()
| def gtsam.examples.HybridCity10000.Experiment.__init__ |
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self, |
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str |
filename, |
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float |
marginal_threshold = 0.9999, |
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int |
max_loop_count = 150, |
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int |
update_frequency = 3, |
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int |
max_num_hypotheses = 10, |
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int |
relinearization_frequency = 10, |
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bool |
plot_hypotheses = False |
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) |
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◆ hybrid_loop_closure_factor()
| def gtsam.examples.HybridCity10000.Experiment.hybrid_loop_closure_factor |
( |
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self, |
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loop_counter, |
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key_s, |
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key_t, |
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Pose2 |
measurement |
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) |
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Create a hybrid loop closure factor where
0 - loose noise model and 1 - loop noise model.
Definition at line 201 of file HybridCity10000.py.
◆ hybrid_odometry_factor()
| HybridNonlinearFactor gtsam.examples.HybridCity10000.Experiment.hybrid_odometry_factor |
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self, |
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key_s, |
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key_t, |
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m, |
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pose_array |
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) |
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Create hybrid odometry factor with discrete measurement choices.
Definition at line 216 of file HybridCity10000.py.
◆ plot_all_hypotheses()
| def gtsam.examples.HybridCity10000.Experiment.plot_all_hypotheses |
( |
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self, |
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discrete_keys, |
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num_poses, |
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num_iters = 0 |
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) |
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◆ reinitialize()
| float gtsam.examples.HybridCity10000.Experiment.reinitialize |
( |
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self | ) |
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◆ run()
| def gtsam.examples.HybridCity10000.Experiment.run |
( |
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self | ) |
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◆ save_results()
| def gtsam.examples.HybridCity10000.Experiment.save_results |
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self, |
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result, |
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final_key, |
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time_list |
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) |
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◆ smoother_update()
| float gtsam.examples.HybridCity10000.Experiment.smoother_update |
( |
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self, |
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max_num_hypotheses |
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) |
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◆ write_result()
| def gtsam.examples.HybridCity10000.Experiment.write_result |
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self, |
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result, |
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num_poses, |
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filename = "Hybrid_city10000.txt" |
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) |
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Write the result of optimization to file.
Args:
result (Values): he Values object with the final result.
num_poses (int): The number of poses to write to the file.
filename (str): The file name to save the result to.
Definition at line 448 of file HybridCity10000.py.
◆ write_timing_info()
| def gtsam.examples.HybridCity10000.Experiment.write_timing_info |
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self, |
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time_list, |
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time_filename = "Hybrid_City10000_time.txt" |
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) |
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◆ dataset_
| gtsam.examples.HybridCity10000.Experiment.dataset_ |
◆ initial_
| gtsam.examples.HybridCity10000.Experiment.initial_ |
◆ max_loop_count
| gtsam.examples.HybridCity10000.Experiment.max_loop_count |
◆ max_num_hypotheses
| gtsam.examples.HybridCity10000.Experiment.max_num_hypotheses |
◆ new_factors_
| gtsam.examples.HybridCity10000.Experiment.new_factors_ |
◆ plot_hypotheses
| gtsam.examples.HybridCity10000.Experiment.plot_hypotheses |
◆ relinearization_frequency
| gtsam.examples.HybridCity10000.Experiment.relinearization_frequency |
◆ smoother_
| gtsam.examples.HybridCity10000.Experiment.smoother_ |
◆ update_frequency
| gtsam.examples.HybridCity10000.Experiment.update_frequency |
The documentation for this class was generated from the following file: