12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Modernize/format

release/4.3a0
Frank Dellaert 2024-10-26 10:19:13 -07:00
parent 5eb858b729
commit a8a229c10c
1 changed files with 21 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
python/gtsam/examples/SFMExample.py
View File

@ -9,20 +9,22 @@ See LICENSE for the license information
A structure-from-motion problem on a simulated dataset
"""
import gtsam
import matplotlib.pyplot as plt
import numpy as np
import gtsam
from gtsam import symbol_shorthand
L = symbol_shorthand.L
X = symbol_shorthand.X
from gtsam.examples import SFMdata
from gtsam import (Cal3_S2, DoglegOptimizer,
GenericProjectionFactorCal3_S2, Marginals,
NonlinearFactorGraph, PinholeCameraCal3_S2, Point3,
Pose3, PriorFactorPoint3, PriorFactorPose3, Rot3, Values)
from gtsam.utils import plot
from gtsam import (Cal3_S2, DoglegOptimizer, GenericProjectionFactorCal3_S2,
Marginals, NonlinearFactorGraph, PinholeCameraCal3_S2,
PriorFactorPoint3, PriorFactorPose3, Values)
def main():
"""
@ -43,7 +45,7 @@ def main():
Finally, once all of the factors have been added to our factor graph, we will want to
solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
GTSAM includes several nonlinear optimizers to perform this step. Here we will use a
trust-region method known as Powell's Degleg
trust-region method known as Powell's Dogleg
The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
nonlinear functions around an initial linearization point, then solve the linear system
@ -78,8 +80,7 @@ def main():
camera = PinholeCameraCal3_S2(pose, K)
for j, point in enumerate(points):
measurement = camera.project(point)
factor = GenericProjectionFactorCal3_S2(
measurement, measurement_noise, X(i), L(j), K)
factor = GenericProjectionFactorCal3_S2(measurement, measurement_noise, X(i), L(j), K)
graph.push_back(factor)
# Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
@ -88,28 +89,29 @@ def main():
point_noise = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
factor = PriorFactorPoint3(L(0), points[0], point_noise)
graph.push_back(factor)
graph.print('Factor Graph:\n')
graph.print("Factor Graph:\n")
# Create the data structure to hold the initial estimate to the solution
# Intentionally initialize the variables off from the ground truth
initial_estimate = Values()
rng = np.random.default_rng()
for i, pose in enumerate(poses):
transformed_pose = pose.retract(0.1*np.random.randn(6,1))
transformed_pose = pose.retract(0.1 * rng.standard_normal(6).reshape(6, 1))
initial_estimate.insert(X(i), transformed_pose)
for j, point in enumerate(points):
transformed_point = point + 0.1*np.random.randn(3)
transformed_point = point + 0.1 * rng.standard_normal(3)
initial_estimate.insert(L(j), transformed_point)
initial_estimate.print('Initial Estimates:\n')
initial_estimate.print("Initial Estimates:\n")
# Optimize the graph and print results
params = gtsam.DoglegParams()
params.setVerbosity('TERMINATION')
params.setVerbosity("TERMINATION")
optimizer = DoglegOptimizer(graph, initial_estimate, params)
print('Optimizing:')
print("Optimizing:")
result = optimizer.optimize()
result.print('Final results:\n')
print('initial error = {}'.format(graph.error(initial_estimate)))
print('final error = {}'.format(graph.error(result)))
result.print("Final results:\n")
print("initial error = {}".format(graph.error(initial_estimate)))
print("final error = {}".format(graph.error(result)))
marginals = Marginals(graph, result)
plot.plot_3d_points(1, result, marginals=marginals)
@ -117,5 +119,6 @@ def main():
plot.set_axes_equal(1)
plt.show()
if __name__ == '__main__':
if __name__ == "__main__":
main()