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Functions94 in github.com/ChengeYang/Probabilistic-Robotics-Algorithms

⨍ Functions 94◇ Types & classes 14
↓ 4 callersMethodinitialization
Input: init_state: initial state (from first ground truth data) [timestamp_0, x_0, y_0, θ_0]
4.Fast_SLAM_1/lib/particle.py:30
↓ 3 callersMethodcompute_expected_measurement
Compute the expected range and bearing given current robot state and landmark state. Measurement model: (expected measuremen
5.Fast_SLAM_2/lib/measurement.py:28
↓ 3 callersMethodcompute_landmark_jacobian
Computing the Jacobian wrt landmark state X_l. Jacobian of measurement: only take derivatives of landmark X_l.
5.Fast_SLAM_2/lib/measurement.py:101
↓ 3 callersMethodcompute_landmark_jacobian
Computing the Jacobian wrt landmark state X_l. Jacobian of measurement: only take derivatives of landmark X_l.
4.Fast_SLAM_1/lib/measurement.py:69
↓ 2 callersMethodcompute_expected_measurement
Compute the expected range and bearing given current robot state and landmark state. Measurement model: (expected measuremen
4.Fast_SLAM_1/lib/measurement.py:22
↓ 2 callersMethodcompute_robot_jacobian
Computing the Jacobian wrt robot state X_t. Jacobian of measurement: only take derivatives of robot X_t.
5.Fast_SLAM_2/lib/measurement.py:75
↓ 2 callersMethodinitialization
Input: init_state: initial state (from first ground truth data) [timestamp_0, x_0, y_0, θ_0]
5.Fast_SLAM_2/lib/particle.py:30
↓ 2 callersMethodinitialize_landmark
Initialize landmark mean and covariance for one landmark of a given particle. This landmark is the first time to be observed.
4.Fast_SLAM_1/lib/measurement.py:95
↓ 2 callersMethodinitialize_particle
Add motion noise to the robot state in the given particle object. Input: particle: Particle() object which has been init
4.Fast_SLAM_1/lib/motion.py:24
↓ 2 callersMethodlandmark_update
Implementation for Fast SLAM 1.0. Update landmark mean and covariance for one landmarks of a given particle. This lan
4.Fast_SLAM_1/lib/measurement.py:131
↓ 2 callersMethodload_data
Load data from UTIAS Multi-Robot Cooperative Localization and Mapping Dataset. Input: dataset: directory of the
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:30
↓ 2 callersMethodmotion_update
(self, current_state, control)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:148
↓ 2 callersMethodplot_data
(self)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:307
↓ 2 callersMethodplot_data
Plot all data through matplotlib. Conduct animation as the algorithm runs. Input: None. Output:
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:278
↓ 2 callersMethodrobot_update
Update robot pose through sampling motion model for all particles. Input: control: control input U_t.
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:134
↓ 2 callersMethodsample_motion_model
Implementation for Fast SLAM 1.0. Sample next state X_t from current state X_t-1 and control U_t with added motion noise.
4.Fast_SLAM_1/lib/motion.py:73
↓ 2 callersMethodstate_update
Update the robot and landmark states by taking average among all particles. Input: None. Output:
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:231
↓ 1 callersMethodcompute_correspondence
Implementation for Fast SLAM 2.0. Update the robot pose of a particle to a given pose sample. Then compute the likelihood of
5.Fast_SLAM_2/lib/measurement.py:284
↓ 1 callersMethodcompute_correspondence
Implementation for Fast SLAM 1.0. Compute the likelihood of correspondence for between a measurement and a given landmark.
4.Fast_SLAM_1/lib/measurement.py:176
↓ 1 callersMethodcompute_expected_landmark_state
Compute the expected landmark location [x, y] given current robot state and measurement data. Expected landmark state: inver
5.Fast_SLAM_2/lib/measurement.py:52
↓ 1 callersMethodcompute_expected_landmark_state
Compute the expected landmark location [x, y] given current robot state and measurement data. Expected landmark state: inver
4.Fast_SLAM_1/lib/measurement.py:46
↓ 1 callersMethoddata_association
For a particle, compute likelihood of correspondence for all observed landmarks. Choose landmark according to ML (Maximum Lik
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:192
↓ 1 callersMethoddata_association
(self, measurement)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:157
↓ 1 callersMethoddata_association
For a particle, compute likelihood of correspondence for all observed landmarks. Choose landmark according to ML (Maximum Lik
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:185
↓ 1 callersMethodimportance_sampling
Resample all particles through the importance factors. Input: None. Output: None.
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:259
↓ 1 callersMethodimportance_sampling
Resample all particles through the importance factors. Input: None. Output: None.
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:206
↓ 1 callersMethodimportance_sampling
Resample all particles through the importance factors. Input: None. Output: None.
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:243
↓ 1 callersMethodimportance_sampling
(self)
1.Localization/PF_Localization_known_correspondences.py:172
↓ 1 callersMethodinitialization
(self, R, Q)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:87
↓ 1 callersMethodinitialization
(self, R, Q)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:85
↓ 1 callersMethodinitialization
(self, R, Q)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:89
↓ 1 callersMethodinitialization
(self, num_particles, motion_noise, measurement_noise)
1.Localization/PF_Localization_known_correspondences.py:81
↓ 1 callersMethodinitialization
(self, R, Q)
1.Localization/EKF_Localization_known_correspondences.py:74
↓ 1 callersMethodinitialize_landmark
Initialize landmark mean and covariance for one landmark of a given particle. This landmark is the first time to be observed.
5.Fast_SLAM_2/lib/measurement.py:184
↓ 1 callersMethodinitialize_particle
Add motion noise to the robot state in the given particle object. Input: particle: Particle() object which has been init
5.Fast_SLAM_2/lib/motion.py:28
↓ 1 callersMethodlandmark_update
Implementation for Fast SLAM 2.0. Update landmark mean and covariance, as well as the particle's weight for one landmarks of
5.Fast_SLAM_2/lib/measurement.py:221
↓ 1 callersMethodlinearize
(self)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:113
↓ 1 callersMethodload_data
Load data from UTIAS Multi-Robot Cooperative Localization and Mapping Dataset. Input: dataset: directory of the
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:37
↓ 1 callersMethodload_data
(self, dataset, start_frame, end_frame)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:31
↓ 1 callersMethodload_data
(self, dataset, start_frame, end_frame)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:30
↓ 1 callersMethodload_data
(self, dataset, start_frame, end_frame)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:27
↓ 1 callersMethodload_data
(self, dataset, end_frame)
1.Localization/PF_Localization_known_correspondences.py:34
↓ 1 callersMethodload_data
(self, dataset, end_frame)
1.Localization/EKF_Localization_known_correspondences.py:27
↓ 1 callersMethodmeasurement_update
(self, measurement)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:243
↓ 1 callersMethodmeasurement_update
(self, measurement)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:154
↓ 1 callersMethodmeasurement_update
(self, measurement)
1.Localization/PF_Localization_known_correspondences.py:133
↓ 1 callersMethodmeasurement_update
(self, measurement)
1.Localization/EKF_Localization_known_correspondences.py:126
↓ 1 callersMethodmeasurement_update_linearize
(self, current_state, measurement)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:210
↓ 1 callersMethodmotion_linearize
(self, current_state, next_state, control)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:174
↓ 1 callersMethodmotion_update
Conduct motion update for a given particle from current state X_t-1 and control U_t. Motion Model (simplified): Stat
5.Fast_SLAM_2/lib/motion.py:43
↓ 1 callersMethodmotion_update
(self, control)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:109
↓ 1 callersMethodmotion_update
(self, control)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:106
↓ 1 callersMethodmotion_update
Conduct motion update for a given particle from current state X_t-1 and control U_t. Motion Model (simplified): Stat
4.Fast_SLAM_1/lib/motion.py:39
↓ 1 callersMethodmotion_update
(self, control)
1.Localization/PF_Localization_known_correspondences.py:100
↓ 1 callersMethodmotion_update
(self, control)
1.Localization/EKF_Localization_known_correspondences.py:88
↓ 1 callersMethodplot_data
Plot all data through matplotlib. Conduct animation as the algorithm runs. Input: None. Output:
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:331
↓ 1 callersMethodplot_data
(self)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:258
↓ 1 callersMethodplot_data
(self)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:228
↓ 1 callersMethodplot_data
(self)
1.Localization/PF_Localization_known_correspondences.py:184
↓ 1 callersMethodplot_data
(self)
1.Localization/EKF_Localization_known_correspondences.py:168
↓ 1 callersMethodreduce
(self)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:281
↓ 1 callersMethodrobot_update
Update robot pose through sampling motion model for all particles. Input: control: control input U_t.
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:141
↓ 1 callersMethodsample_measurement_model
Implementation for Fast SLAM 2.0. Sample next state X_t from current state X_t and measurement Z_t with added measurement cov
5.Fast_SLAM_2/lib/measurement.py:127
↓ 1 callersMethodsample_motion_model
Implementation for Fast SLAM 1.0. Sample next state X_t from current state X_t-1 and control U_t with added motion noise.
5.Fast_SLAM_2/lib/motion.py:77
↓ 1 callersMethodsolve
(self)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:286
↓ 1 callersMethodstate_update
Update the robot and landmark states by taking average among all particles. Input: None. Output:
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:284
↓ 1 callersMethodstate_update
(self)
1.Localization/PF_Localization_known_correspondences.py:179
↓ 1 callersMethodweights_normalization
Normalize weight in all particles so that the sum = 1. Input: None. Output: None.
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:236
↓ 1 callersMethodweights_normalization
Normalize weight in all particles so that the sum = 1. Input: None. Output: None.
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:183
↓ 1 callersMethodweights_normalization
Normalize weight in all particles so that the sum = 1. Input: None. Output: None.
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:220
Method__init__
Input: R: Measurement covariance matrix. Dimension: [3, 3]. Q: Measurement covariance matrix.
5.Fast_SLAM_2/lib/measurement.py:15
Method__init__
(self)
5.Fast_SLAM_2/lib/particle.py:14
Method__init__
Input: R: Measurement covariance matrix. Dimension: [3, 3]. motion_noise: [noise_x, noise_y, noise_the
5.Fast_SLAM_2/lib/motion.py:16
Method__init__
Input: motion_model: ModelModel() object measurement_model: MeasurementModel() object
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:28
Method__init__
(self, dataset, start_frame, end_frame, R, Q)
2.EKF_SLAM/EKF_SLAM_unknown_correspondences.py:17
Method__init__
(self, dataset, start_frame, end_frame, R, Q)
2.EKF_SLAM/EKF_SLAM_known_correspondences.py:17
Method__init__
(self, dataset, start_fram, end_frame, N_iterations, R, Q)
3.Graph_SLAM/Graph_SLAM_known_correspondences.py:17
Method__init__
Input: Q: Measurement covariance matrix. Dimension: [2, 2].
4.Fast_SLAM_1/lib/measurement.py:14
Method__init__
(self)
4.Fast_SLAM_1/lib/particle.py:14
Method__init__
Input: motion_noise: [noise_x, noise_y, noise_theta, noise_v, noise_w] (in meters / rad).
4.Fast_SLAM_1/lib/motion.py:16
Method__init__
Input: motion_model: ModelModel() object measurement_model: MeasurementModel() object
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:21
Method__init__
Input: motion_model: ModelModel() object measurement_model: MeasurementModel() object
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:23
Method__init__
(self, dataset, end_frame, num_particles, motion_noise, measurement_noise)
1.Localization/PF_Localization_known_correspondences.py:20
Method__init__
(self, dataset, end_frame, R, Q)
1.Localization/EKF_Localization_known_correspondences.py:17
Methodinitialization
Initialize robots state, landmark state and all particles. Input: N_particles: number of particles this SLAM algorithms
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:108
Methodinitialization
Initialize robots state, landmark state and all particles. Input: N_particles: number of particles this SLAM algorithms
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:101
Methodinitialization
Initialize robots state, landmark state and all particles. Input: N_particles: number of particles this SLAM algorithms
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:103
Methodlandmark_update
Update landmark mean and covariance for all landmarks of all particles. Based on EKF method. Input: measurement:
5.Fast_SLAM_2/src/Fast_SLAM_2_unknown_correspondences.py:154
Methodlandmark_update
Update landmark mean and covariance for all landmarks of all particles. Based on EKF method. Input: measurement:
4.Fast_SLAM_1/src/Fast_SLAM_1_known_correspondences.py:147
Methodlandmark_update
Update landmark mean and covariance for all landmarks of all particles. Based on EKF method. Input: measurement:
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:149
Methodload_data
Load data from UTIAS Multi-Robot Cooperative Localization and Mapping Dataset. Input: dataset: directory of the
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:32
Methodplot_data
Plot all data through matplotlib. Conduct animation as the algorithm runs. Input: None. Output:
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:315
Methodrobot_update
Update robot pose through sampling motion model for all particles. Input: control: control input U_t.
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:136
Methodstate_update
Update the robot and landmark states by taking average among all particles. Input: None. Output:
4.Fast_SLAM_1/src/Fast_SLAM_1_unknown_correspondences.py:268