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

Error checking

release/4.3a0
Frank Dellaert 2024-10-28 13:00:28 -07:00
parent 39d74d1d2a
commit c0bac3cac5
1 changed files with 23 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
python/gtsam/examples/ViewGraphComparison.py
View File

@ -63,13 +63,24 @@ def simulate_data(points, poses, cal, rng, noise_std):
# Function to compute ground truth matrices
def compute_ground_truth(method, poses, cal):
F1 = FundamentalMatrix(cal.K(), poses[0].between(poses[1]), cal.K())
F2 = FundamentalMatrix(cal.K(), poses[0].between(poses[2]), cal.K())
if method == "FundamentalMatrix":
F1 = FundamentalMatrix(cal.K(), poses[0].between(poses[1]), cal.K())
F2 = FundamentalMatrix(cal.K(), poses[0].between(poses[2]), cal.K())
return F1, F2
elif method == "EssentialMatrix":
E1 = EssentialMatrix.FromPose3(poses[0].between(poses[1]))
E2 = EssentialMatrix.FromPose3(poses[0].between(poses[2]))
# Assert that E1.matrix and F1 are the same
invK = np.linalg.inv(cal.K())
G1 = invK.transpose() @ E1.matrix() @ invK
G2 = invK.transpose() @ E2.matrix() @ invK
assert np.allclose(
G1 / np.linalg.norm(G1), F1.matrix() / np.linalg.norm(F1.matrix())
), "E1 and F1 are not the same"
assert np.allclose(
G2 / np.linalg.norm(G2), F2.matrix() / np.linalg.norm(F2.matrix())
), "E2 and F2 are not the same"
return E1, E2
else:
raise ValueError(f"Unknown method {method}")
@ -261,6 +272,16 @@ def main():
# Build the factor graph
graph = build_factor_graph(method, args.num_cameras, measurements)
# Assert that the initial error is the same for both methods:
if trial == 0 and method == methods[0]:
initial_error = graph.error(initial_estimate[method])
else:
current_error = graph.error(initial_estimate[method])
if not np.allclose(initial_error, current_error):
print(f"Initial error for {method} ({current_error}) does not match ")
print(f"initial error for {methods[0]} ({initial_error})")
exit(1)
# Optimize the graph
result = optimize(graph, initial_estimate[method])