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ba_example.py
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ba_example.py
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# https://github.com/RainerKuemmerle/g2o/blob/master/g2o/examples/ba/ba_demo.cpp
import numpy as np
import g2o
from collections import defaultdict
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--noise",
dest="pixel_noise",
type=float,
default=1.0,
help="noise in image pixel space (default: 1.0)",
)
parser.add_argument(
"--outlier",
dest="outlier_ratio",
type=float,
default=0.0,
help="probability of spuroius observation (default: 0.0)",
)
parser.add_argument(
"--robust", dest="robust_kernel", action="store_true", help="use robust kernel"
)
parser.add_argument("--dense", action="store_true", help="use dense solver")
parser.add_argument("--seed", type=int, help="random seed", default=0)
args = parser.parse_args()
def main():
print("BA demo")
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverDenseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
focal_length = 1000
principal_point = (320, 240)
cam = g2o.CameraParameters(focal_length, principal_point, 0)
cam.set_id(0)
optimizer.add_parameter(cam)
true_points = np.hstack(
[
np.random.random((500, 1)) * 3 - 1.5,
np.random.random((500, 1)) - 0.5,
np.random.random((500, 1)) + 3,
]
)
true_poses = []
num_pose = 15
for i in range(num_pose):
# pose here means transform points from world coordinates to camera coordinates
pose = g2o.SE3Quat(np.identity(3), [i * 0.04 - 1, 0, 0])
true_poses.append(pose)
v_se3 = g2o.VertexSE3Expmap()
v_se3.set_id(i)
v_se3.set_estimate(pose)
if i < 2:
v_se3.set_fixed(True)
optimizer.add_vertex(v_se3)
print("Poses done")
point_id = num_pose
inliers = dict()
sse = defaultdict(float)
for i, point in enumerate(true_points):
visible = []
for j, pose in enumerate(true_poses):
z = cam.cam_map(pose * point)
if 0 <= z[0] < 640 and 0 <= z[1] < 480:
visible.append((j, z))
if len(visible) < 2:
continue
vp = g2o.VertexPointXYZ()
vp.set_id(point_id)
vp.set_marginalized(True)
vp.set_estimate(point + np.random.randn(3))
optimizer.add_vertex(vp)
inlier = True
for j, z in visible:
if np.random.random() < args.outlier_ratio:
inlier = False
z = np.random.random(2) * [640, 480]
z += np.random.randn(2) * args.pixel_noise
edge = g2o.EdgeProjectXYZ2UV()
edge.set_vertex(0, vp)
edge.set_vertex(1, optimizer.vertex(j))
edge.set_measurement(z)
edge.set_information(np.identity(2))
if args.robust_kernel:
edge.set_robust_kernel(g2o.RobustKernelHuber())
edge.set_parameter_id(0, 0)
optimizer.add_edge(edge)
if inlier:
inliers[point_id] = i
error = vp.estimate() - true_points[i]
sse[0] += np.sum(error ** 2)
point_id += 1
print("num vertices:", len(optimizer.vertices()))
print("num edges:", len(optimizer.edges()))
print("Performing full BA:")
optimizer.initialize_optimization()
optimizer.set_verbose(True)
optimizer.optimize(10)
for i in inliers:
vp = optimizer.vertex(i)
error = vp.estimate() - true_points[inliers[i]]
sse[1] += np.sum(error ** 2)
print("\nRMSE (inliers only):")
print("before optimization:", np.sqrt(sse[0] / len(inliers)))
print("after optimization:", np.sqrt(sse[1] / len(inliers)))
if __name__ == "__main__":
if args.seed > 0:
np.random.seed(args.seed)
main()