def __init__(self):
super().__init__()
# g2o::BlockSlover_6_3(g2o::BlockSolver_6_3::LinearSolverType*)
linear_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(linear_solver)
super().set_algorithm(solver)
# additional parameters
#
self._map = None
#
self.vertex_seq_generator = AtomicCounter()
self.edge_seq_generator = AtomicCounter()
# Point | Frame | Landmark -> Vertex mapping
# inverse vertex query
self._ivq = {}
# (Vertex, Vetex) -> Edge mapping, a sparse matrix
# inverse edges query
self._ieq = {}
#
self.USE_LANDMARKS = False
def bundle_adjustment(ps_1, ps_2, r_mat, t_vec):
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSim3(g2o.LinearSolverCSparseSim3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
pose = g2o.VertexSE3Expmap()
pose.set_estimate(g2o.SE3Quat(np.identity(3), np.zeros((3,))))
pose.set_id(0)
optimizer.add_vertex(pose)
index = 1
for p1, p2 in zip(ps_1, ps_2):
edge = g2o.EdgeStereoSE3ProjectXYZOnlyPose()
edge.cam_project(p2)
edge.set_id(index)
edge.set_vertex(0, pose)
edge.set_measurement(p1)
edge.set_information(np.identity(3))
optimizer.add_edge(edge)
index += 1
optimizer.initialize_optimization()
optimizer.set_verbose(True)
optimizer.optimize(100)
print('T = \n', pose.estimate().matrix())
def __init__(self):
super().__init__()
# solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.BlockSolverSE3(g2o.LinearSolverDenseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super().set_algorithm(solver)
super().set_verbose(True)
def __init__(self, verbose=False):
self.solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
self.solver = g2o.OptimizationAlgorithmLevenberg(self.solver)
self.optimizer = g2o.SparseOptimizer()
self.optimizer.set_verbose(verbose)
self.optimizer.set_algorithm(self.solver)
def __init__(self):
# inherits traits from g2o's Sparse Optimizer class
super().__init__()
# intitialize solver and optimizer
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super().set_algorithm(solver)
def optimize(self):
# create g2o optimizer
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
opt.set_algorithm(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
# add frames to graph
for f in self.frames:
pose = f.pose
#pose = np.linalg.inv(pose)
sbacam = g2o.SBACam(g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]))
sbacam.set_cam(f.K[0][0], f.K[1][1], f.K[0][2], f.K[1][2], 1.0)
v_se3 = g2o.VertexCam()
v_se3.set_id(f.id)
v_se3.set_estimate(sbacam)
v_se3.set_fixed(f.id <= 1)
opt.add_vertex(v_se3)
# add points to frames
PT_ID_OFFSET = 0x10000
for p in self.points:
pt = g2o.VertexSBAPointXYZ()
pt.set_id(p.id + PT_ID_OFFSET)
pt.set_estimate(p.pt[0:3])
pt.set_marginalized(True)
pt.set_fixed(False)
opt.add_vertex(pt)
for f in p.frames:
edge = g2o.EdgeProjectP2MC()
edge.set_vertex(0, pt)
edge.set_vertex(1, opt.vertex(f.id))
uv = f.kpus[f.pts.index(p)]
edge.set_measurement(uv)
edge.set_information(np.eye(2))
edge.set_robust_kernel(robust_kernel)
opt.add_edge(edge)
opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(50)
# put frames back
for f in self.frames:
est = opt.vertex(f.id).estimate()
R = est.rotation().matrix()
t = est.translation()
f.pose = poseRt(R, t)
# put points back
for p in self.points:
est = opt.vertex(p.id + PT_ID_OFFSET).estimate()
p.pt = np.array(est)
def __init__(self):
super(PoseGraphOptimization, self).__init__()
solver = g2o.BlockSolverSE2(g2o.LinearSolverCholmodSE2())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super(PoseGraphOptimization, self).set_algorithm(solver)
self.first_pose_vertex_id = -1 # used to create pose-pose and pose-landmark edges
self.last_pose_vertex_id = -1 # used to create pose-pose and pose-landmark edges
self.landmark_vertex_id = -1 # single landmark vertex for this assignment
self.next_vertex_id = 0 # generic vertex id incrementer, used to generate ids for both pose and landmark vertices
def __init__(self):
self.optimizer = g2o.SparseOptimizer()
# self.solver = g2o.BlockSolverX(g2o.LinearSolverDenseX())
# self.solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
self.solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
self.algorithm = g2o.OptimizationAlgorithmLevenberg(self.solver)
self.optimizer.set_algorithm(self.algorithm)
self.already_initialized = False
self.last_lost = 0
self.lock = ControlableLock(True)
def optimize(self):
# init g2o solver
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
opt.set_algorithm(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
# add frames to graph
for frame in self.frames:
pose = frame.pose
sbacam = g2o.SBACam(
g2o.SE3Quat(frame.pose[0:3, 0:3], frame.pose[0:3, 3]))
# sbacam.set_cam(frame.K[0][0], frame.K[1][1], frame.K[2][0], frame.K[2][1], 1.0)
sbacam.set_cam(1.0, 1.0, 0.0, 0.0, 1.0)
v_se3 = g2o.VertexCam()
v_se3.set_id(frame.id)
v_se3.set_estimate(sbacam)
v_se3.set_fixed(frame.id == 0)
opt.add_vertex(v_se3)
# add points to frames
PT_ID_OFFSET = 0x10000
for point in self.points:
pt = g2o.VertexSBAPointXYZ()
pt.set_id(point.id + PT_ID_OFFSET)
pt.set_estimate(point.pt[0:3])
pt.set_marginalized(True)
pt.set_fixed(False)
opt.add_vertex(pt)
for frame in point.frames:
edge = g2o.EdgeProjectP2MC()
edge.set_vertex(0, pt)
edge.set_vertex(1, opt.vertex(frame.id))
uv = frame.kps[frame.pts.index(point)]
edge.set_measurement(uv)
edge.set_information(np.eye(2))
edge.set_robust_kernel(robust_kernel)
opt.add_edge(edge)
# opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(20)
# add pose back to frame
for frame in self.frames:
est = opt.vertex(frame.id).estimate()
R = est.rotation().matrix()
t = est.translation()
frame.pose = poseRt(R, t)
def __init__(self, max_iterations=100, verbose=False, online=False):
solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
self.optimizer = g2o.SparseOptimizer()
self.optimizer.set_verbose(True)
self.optimizer.set_algorithm(solver)
self.current_pose = None
self.max_iterations = max_iterations
self.verbose = verbose
self.online = online
self.clear()
def oprimizer(self):
## Init optimizer
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
## add frames to graph
for f in self.frames:
v_se3 = g2o.VertexSE3()
v_se3.set_id(f.id)
v_se3.set_estimate(f.pose)
v_se3.set_fixed(f.id == 0)
optimizer.add_vertex(v_se3)
def prepare_optimiser(self, verbose):
# create g2o optimizer
self.opt = g2o.SparseOptimizer()
self.solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
self.solver = g2o.OptimizationAlgorithmLevenberg(self.solver)
self.opt.set_algorithm(self.solver)
# add normalized camera
cam = g2o.CameraParameters(1.0, (0.0, 0.0), 0)
cam.set_id(0)
self.opt.add_parameter(cam)
self.robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
self.graph_frames, self.graph_points = {}, {}
if verbose:
self.opt.set_verbose(True)
def main():
solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer = g2o.SparseOptimizer()
optimizer.set_verbose(True)
optimizer.set_algorithm(solver)
optimizer.load(args.input)
print('num vertices:{}'.format(len(optimizer.vertices())))
print('num edges:{}'.format(len(optimizer.edges())))
for i in range(len(optimizer.vertices())):
vp_se3 = optimizer.vertex(i)
print(dir(vp_se3))
print(vp_se3.id)
break
def __init__(self, ):
super().__init__()
# Higher confident (better than CHOLMOD, according to
# paper "3-D Mapping With an RGB-D Camera")
solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super().set_algorithm(solver)
# Convergence Criterion
terminate = g2o.SparseOptimizerTerminateAction()
terminate.set_gain_threshold(1e-6)
super().add_post_iteration_action(terminate)
# Robust cost Function (Huber function) delta
self.delta = np.sqrt(5.991)
self.aborted = False
def main():
#solver = g2o.BlockSolverX(g2o.LinearSolverCholmodX())
solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer = g2o.SparseOptimizer()
optimizer.set_verbose(True)
optimizer.set_algorithm(solver)
optimizer.load(args.input)
print('num vertices:', len(optimizer.vertices()))
print('num edges:', len(optimizer.edges()), end='\n\n')
optimizer.initialize_optimization()
optimizer.optimize(args.max_iterations)
if len(args.output) > 0:
optimizer.save(args.output)
def __init__(self):
super().__init__()
# g2o::BlockSlover_6_3(g2o::BlockSolver_6_3::LinearSolverType*)
linear_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(linear_solver)
super().set_algorithm(solver)
# additional parameters
terminate = g2o.SparseOptimizerTerminateAction()
terminate.set_gain_threshold(1e-6)
super().add_post_iteration_action(terminate)
#
self._map = None
#
self.frame = None
#
self.points = None
#
self.vSE3 = None
#
self.measurements = None
#
self.vertex_seq_generator = AtomicCounter()
self.edge_seq_generator = AtomicCounter()
# Point | Frame | Landmark -> Vertex mapping
# inverse vertex query
self._ivq = {}
# (Vertex, Vetex) -> Edge mapping, a sparse matrix
# inverse edges query
self._ieq = {}
#
self.USE_LANDMARKS = False
#
self.edges = []
def optimize(self, max_iters):
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
opt.set_algorithm(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
# adding frames to graph
for f in self.frames:
sbacam = g2o.SBACam(g2o.SE3Quat(f.pose[0:3, 0:3], f.pose[0:3, 3]))
sbacam.set_cam(f.T[0][0], f.T[1][1], f.T[2][0], f.T[2][1], 1.0)
v_se3 = g2o.VertexCam()
v_se3.set_id(f.id)
v_se3.set_estimate(sbacam)
v_se3.set_fixed(f.id == 0)
opt.add_vertex(v_se3)
# add points to graph
for p in self.points:
pt = g2o.VertexSBAPointXYZ()
pt.set_id(p.id + 0x10000)
pt.set_estimate(p.location[0:3])
pt.set_marginalized(True)
pt.set_fixed(False)
opt.add_vertex(pt)
# add connections between frames that contain a point in the graph
for f in p.frames:
edge = g2o.EdgeProjectP2MC()
edge.set_vertex(0, pt)
edge.set_vertex(1, opt.vertex(f.id))
uv = f.kpts[f.pts.index(p)]
edge.set_measurement(uv)
edge.set_information(np.eye(2))
edge.set_robust_kernel(robust_kernel)
opt.add_edge(edge)
opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(max_iters)
def optimize(self):
# optimizer
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
opt.set_algorithm(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
for f in self.frames:
sbacam = g2o.SBACam(g2o.SE3Quat(f.pose[:3, :3], f.pose[:3, 3]))
sbacam.set_cam(f.K[0][0], f.K[1][1], f.K[2][0], f.K[2][1], 1.0)
v_se3 = g2o.VertexCam()
v_se3.set_id(f.id)
v_se3.set_estimate(sbacam)
v_se3.set_fixed(f.id == 0)
opt.add_vertex(v_se3)
for p in self.points:
pt = g2o.VertexSBAPointXYZ()
pt.set_id(p.id + 0x10000)
pt.set_estimate(p.pt[0:3])
pt.set_marginalized(True)
pt.set_fixed(False)
opt.add_vertex(pt)
for f in p.frames:
edge = g2o.EdgeProjectP2MC()
edge.set_vertex(0, pt)
edge.set_vertex(1, opt.vertex(f.id))
uv = f.kps[f.pts.index(p)]
edge.set_measurement(uv)
edge.set_information(np.eye(2))
edge.set_robust_kernel(robust_kernel)
opt.add_edge(edge)
opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(20)
def bundle_adjustment(points_3d, points_2d, r_mat, t_vec):
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
pose = g2o.VertexSE3Expmap()
pose.set_estimate(g2o.SE3Quat(r_mat, t_vec.reshape((3, ))))
pose.set_id(0)
optimizer.add_vertex(pose)
index = 1
for p_3d in points_3d:
point = g2o.VertexSBAPointXYZ()
point.set_id(index)
point.set_estimate(p_3d)
point.set_marginalized(True)
optimizer.add_vertex(point)
index += 1
camera = g2o.CameraParameters(K[0, 0], np.array([K[0, 2], K[1, 2]]), 0)
camera.set_id(0)
optimizer.add_parameter(camera)
index = 1
for p_2d in points_2d:
edge = g2o.EdgeProjectXYZ2UV()
edge.set_id(index)
edge.set_vertex(0, optimizer.vertex(index))
edge.set_vertex(1, pose)
edge.set_measurement(p_2d)
edge.set_parameter_id(0, 0)
edge.set_information(np.identity(2))
optimizer.add_edge(edge)
index += 1
optimizer.initialize_optimization()
optimizer.set_verbose(True)
optimizer.optimize(100)
print('T = \n', pose.estimate().matrix())
def optimize(self):
# create g2o optimizer
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
opt.set_algorithm(solver)
robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991))
if LOCAL_WINDOW is None:
local_frames = self.frames
else:
local_frames = self.frames[-LOCAL_WINDOW:]
# add frames to graph
for f in self.frames:
pose = f.pose
sbacam = g2o.SBACam(g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]))
sbacam.set_cam(f.K[0][0], f.K[1][1], f.K[0][2], f.K[1][2], 1.0)
v_se3 = g2o.VertexCam()
v_se3.set_id(f.id)
v_se3.set_estimate(sbacam)
v_se3.set_fixed(f.id <= 1 or f not in local_frames)
opt.add_vertex(v_se3)
# add points to frames
PT_ID_OFFSET = 0x10000
for p in self.points:
if not any([f in local_frames for f in p.frames]):
continue
pt = g2o.VertexSBAPointXYZ()
pt.set_id(p.id + PT_ID_OFFSET)
pt.set_estimate(p.pt[0:3])
pt.set_marginalized(True)
pt.set_fixed(False)
opt.add_vertex(pt)
for f in p.frames:
edge = g2o.EdgeProjectP2MC()
edge.set_vertex(0, pt)
edge.set_vertex(1, opt.vertex(f.id))
uv = f.kpus[f.pts.index(p)]
edge.set_measurement(uv)
edge.set_information(np.eye(2))
edge.set_robust_kernel(robust_kernel)
opt.add_edge(edge)
#opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(50)
# put frames back
for f in self.frames:
est = opt.vertex(f.id).estimate()
R = est.rotation().matrix()
t = est.translation()
f.pose = poseRt(R, t)
# put points back (and cull)
new_points = []
for p in self.points:
vert = opt.vertex(p.id + PT_ID_OFFSET)
if vert is None:
new_points.append(p)
continue
est = vert.estimate()
# 2 match point that's old
old_point = len(p.frames) == 2 and p.frames[-1] not in local_frames
# compute reprojection error
errs = []
for f in p.frames:
uv = f.kpus[f.pts.index(p)]
proj = np.dot(np.dot(f.K, np.linalg.inv(f.pose)[:3]),
np.array([est[0], est[1], est[2], 1.0]))
proj = proj[0:2] / proj[2]
errs.append(np.linalg.norm(proj-uv))
# cull
if (old_point and np.mean(errs) > 30) or np.mean(errs) > 100:
p.delete()
continue
p.pt = np.array(est)
new_points.append(p)
self.points = new_points
return opt.chi2()
def main():
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
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)
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.VertexSBAPointXYZ()
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:
print(i)
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)))
def optimize(self, K, frames, fix_points=False):
PT_OFFSET = 10000
opt = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
opt.set_algorithm(solver)
rk = g2o.RobustKernelHuber(np.sqrt(5.991))
# only optimize these poses and their Points
local_frames = self.frames[-frames:]
# a vertex for each camera pose
for f in self.frames:
assert (f.id < PT_OFFSET)
quat = g2o.SE3Quat(f.pose[:3, :3], f.pose[:3, 3])
sbacam = g2o.SBACam(quat)
sbacam.set_cam(K[0, 0], K[1, 1], K[0, 2], K[1, 2],
1.0) # for pixel input
v = g2o.VertexCam()
v.set_id(f.id)
v.set_estimate(sbacam)
v.set_fixed(f.id < 2 or f not in local_frames)
opt.add_vertex(v)
# a vertex for each point in cloud
for p in self.points:
# if p.frames[-1] not in local_frames:
if not any([f in local_frames for f in p.frames]):
continue
vp = g2o.VertexSBAPointXYZ()
vp.set_id(PT_OFFSET + p.id)
vp.set_estimate(p.pt3d)
vp.set_marginalized(True)
vp.set_fixed(fix_points)
opt.add_vertex(vp)
# edge connects every camera with every point
for f in p.frames:
edge = g2o.EdgeProjectP2MC()
# edge = g2o.EdgeSE3ProjectXYZ()
edge.set_vertex(0, vp)
edge.set_vertex(1, opt.vertex(f.id))
idx = f.pts.index(p)
edge.set_measurement(f.kpus[idx])
edge.set_information(np.eye(2))
edge.set_robust_kernel(rk)
opt.add_edge(edge)
# opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(20)
# print("chi2", opt.active_chi2())
# get back optimized poses
self.opath = []
for f in self.frames:
f_est = opt.vertex(f.id).estimate()
R = f_est.rotation().matrix()
t = f_est.translation()
Rt = np.eye(4)
Rt[:3, :3] = R
Rt[:3, 3] = t.T
# print(Rt)
self.opath.append(t)
f.pose = Rt
if fix_points:
return
new_points = []
# add back or cull
for p in self.points:
vp = opt.vertex(PT_OFFSET + p.id)
if vp is None: # not updated
new_points.append(p)
continue
vp = opt.vertex(PT_OFFSET + p.id)
p_est = vp.estimate()
# if (len(p.frames) <= 5) and (p.frames[-1] not in local_frames):
# p.delete()
# continue
errors = []
for f in p.frames:
f_est = opt.vertex(f.id).estimate()
measured = f.kpus[f.pts.index(p)]
projected = f_est.w2i.dot(np.hstack(
[p_est, [1]])) # only works because is VertexCam
projected = projected[:2] / projected[
2] # don't forget to h**o
errors.append(np.linalg.norm(measured - projected))
error = np.mean(errors) # mean of squares - over all frames
if error > 1.0: # px
# print(f"error {error}, dropping")
p.delete()
else:
p.pt3d = np.array(p_est)
new_points.append(p)
print("Dropping:",
len(self.points) - len(new_points), ", Remaining:",
len(new_points))
self.points = new_points
import sys
import numpy as np
import math
sys.path.append("../../")
from config import Config
import g2o
opt = g2o.SparseOptimizer()
block_solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
solver = g2o.OptimizationAlgorithmLevenberg(block_solver)
opt.set_algorithm(solver)
flag = g2o.Flag()
print('flag: ', flag.value)
opt.set_force_stop_flag(flag)
flag.value = False
print('opt flag: ', opt.force_stop_flag())
flag.value = True
print('opt flag: ', opt.force_stop_flag())
def main():
optimizer = g2o.SparseOptimizer()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
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
])
focal_length = [500, 500]
principal_point = [320, 240]
baseline = 0.075
camera = g2o.CameraParameters(500, [320, 240], 0.075)
camera.set_id(10000)
optimizer.add_parameter(camera)
true_poses = []
num_pose = 5
for i in range(num_pose):
# pose here 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)
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 = camera.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.VertexSBAPointXYZ()
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.array([
np.random.uniform(64, 640),
np.random.uniform(0, 480),
np.random.uniform(0, 64)
]) # disparity
z[2] = z[0] - z[2]
z += np.random.randn(2) * args.pixel_noise
edge = g2o.Edge_XYZ_VSC()
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('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('\nCamera focal: \n{}\n'.format(camera.focal_length))
print('\nCamera principal point: \n{}\n'.format(camera.principal_point))
print('\nRMSE (inliers only):')
print('before optimization:', np.sqrt(sse[0] / len(inliers)))
print('after optimization:', np.sqrt(sse[1] / len(inliers)))
def __init__(self):
super(PoseGraphOptimization, self).__init__()
# solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.BlockSolverSE2(g2o.LinearSolverCholmodSE2())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super(PoseGraphOptimization, self).set_algorithm(solver)
def __init__(self, ):
super().__init__()
solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
super().set_algorithm(solver)
def local_bundle_adjustment(keyframes, points, keyframes_ref=[], fixed_points=False, verbose=False, rounds=10, abort_flag=g2o.Flag(), map_lock=None):
# create g2o optimizer
opt = g2o.SparseOptimizer()
block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
#block_solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
#block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(block_solver)
opt.set_algorithm(solver)
opt.set_force_stop_flag(abort_flag)
#robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) # chi-square 2 DOFs
thHuberMono = math.sqrt(5.991); # chi-square 2 DOFS
graph_keyframes, graph_points = {}, {}
# add frame vertices to graph
for kf in keyframes:
if kf.is_bad:
continue
#print('adding vertex frame ', f.id, ' to graph')
se3 = g2o.SE3Quat(kf.Rcw, kf.tcw)
v_se3 = g2o.VertexSE3Expmap()
v_se3.set_estimate(se3)
v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!)
v_se3.set_fixed(kf.kid==0) # (use f.kid here!)
opt.add_vertex(v_se3)
graph_keyframes[kf] = v_se3
# confirm pose correctness
#est = v_se3.estimate()
#assert np.allclose(pose[0:3, 0:3], est.rotation().matrix())
#assert np.allclose(pose[0:3, 3], est.translation())
# add reference frame vertices to graph
for kf in keyframes_ref:
if kf.is_bad:
continue
#print('adding vertex frame ', f.id, ' to graph')
se3 = g2o.SE3Quat(kf.Rcw, kf.tcw)
v_se3 = g2o.VertexSE3Expmap()
v_se3.set_estimate(se3)
v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!)
v_se3.set_fixed(True)
opt.add_vertex(v_se3)
graph_keyframes[kf] = v_se3
graph_edges = {}
num_edges = 0
num_bad_edges = 0
# add point vertices to graph
for p in points:
assert(p is not None)
if p.is_bad: # do not consider bad points
continue
if not any([f in keyframes for f in p.keyframes()]):
Printer.orange('point %d without a viewing keyframe in input keyframes!!' %(p.id))
#Printer.orange(' keyframes: ',p.observations_string())
continue
#print('adding vertex point ', p.id,' to graph')
v_p = g2o.VertexSBAPointXYZ()
v_p.set_id(p.id * 2 + 1) # odd ids
v_p.set_estimate(p.pt[0:3])
v_p.set_marginalized(True)
v_p.set_fixed(fixed_points)
opt.add_vertex(v_p)
graph_points[p] = v_p
# add edges
for kf, p_idx in p.observations():
if kf.is_bad:
continue
if kf not in graph_keyframes:
continue
if __debug__:
p_f = kf.get_point_match(p_idx)
if p_f != p:
print('frame: ', kf.id, ' missing point ', p.id, ' at index p_idx: ', p_idx)
if p_f is not None:
print('p_f:', p_f)
print('p:',p)
assert(kf.get_point_match(p_idx) is p)
#print('adding edge between point ', p.id,' and frame ', f.id)
edge = g2o.EdgeSE3ProjectXYZ()
edge.set_vertex(0, v_p)
edge.set_vertex(1, graph_keyframes[kf])
edge.set_measurement(kf.kpsu[p_idx])
invSigma2 = Frame.feature_manager.inv_level_sigmas2[kf.octaves[p_idx]]
edge.set_information(np.eye(2)*invSigma2)
edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono))
edge.fx = kf.camera.fx
edge.fy = kf.camera.fy
edge.cx = kf.camera.cx
edge.cy = kf.camera.cy
opt.add_edge(edge)
graph_edges[edge] = (p,kf,p_idx) # one has kf.points[p_idx] == p
num_edges += 1
if verbose:
opt.set_verbose(True)
if abort_flag.value:
return -1,0
# initial optimization
opt.initialize_optimization()
opt.optimize(5)
if not abort_flag.value:
chi2Mono = 5.991 # chi-square 2 DOFs
# check inliers observation
for edge, edge_data in graph_edges.items():
p = edge_data[0]
if p.is_bad:
continue
if edge.chi2() > chi2Mono or not edge.is_depth_positive():
edge.set_level(1)
num_bad_edges += 1
edge.set_robust_kernel(None)
# optimize again without outliers
opt.initialize_optimization()
opt.optimize(rounds)
# search for final outlier observations and clean map
num_bad_observations = 0 # final bad observations
outliers_data = []
for edge, edge_data in graph_edges.items():
p, kf, p_idx = edge_data
if p.is_bad:
continue
assert(kf.get_point_match(p_idx) is p)
if edge.chi2() > chi2Mono or not edge.is_depth_positive():
num_bad_observations += 1
outliers_data.append(edge_data)
if map_lock is None:
map_lock = RLock() # put a fake lock
with map_lock:
# remove outlier observations
for d in outliers_data:
p, kf, p_idx = d
p_f = kf.get_point_match(p_idx)
if p_f is not None:
assert(p_f is p)
p.remove_observation(kf,p_idx)
# the following instruction is now included in p.remove_observation()
#f.remove_point(p) # it removes multiple point instances (if these are present)
#f.remove_point_match(p_idx) # this does not remove multiple point instances, but now there cannot be multiple instances any more
# put frames back
for kf in graph_keyframes:
est = graph_keyframes[kf].estimate()
#R = est.rotation().matrix()
#t = est.translation()
#f.update_pose(poseRt(R, t))
kf.update_pose(g2o.Isometry3d(est.orientation(), est.position()))
# put points back
if not fixed_points:
for p in graph_points:
p.update_position(np.array(graph_points[p].estimate()))
p.update_normal_and_depth(force=True)
active_edges = num_edges-num_bad_edges
mean_squared_error = opt.active_chi2()/active_edges
return mean_squared_error, num_bad_observations/max(num_edges,1)
def __init__(self):
self._optimizer = g2o.SparseOptimizer()
block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
optimization_algorithm = g2o.OptimizationAlgorithmLevenberg(
block_solver)
self._optimizer.set_algorithm(optimization_algorithm)
def main():
optimizer = g2o.SparseOptimizer()
# solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3())
solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
# solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
solver = g2o.OptimizationAlgorithmLevenberg(solver)
optimizer.set_algorithm(solver)
# Convergence Criterion
terminate = g2o.SparseOptimizerTerminateAction()
terminate.set_gain_threshold(1e-6)
optimizer.add_post_iteration_action(terminate)
# Robust cost Function (Huber function) delta
delta = np.sqrt(5.991)
true_points = np.hstack([
np.random.random((25, 1)) * 3 - 1.5,
np.random.random((25, 1)) - 0.5,
np.random.random((25, 1)) + 3])
fx = 600.
fy = 600.
cx = 320.
cy = 240.
principal_point = (cx, cy)
cam = g2o.CameraParameters(fx, principal_point, 0)
true_poses = []
num_pose = 10
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(optimizer.vertices())
point_id = num_pose
inliers = dict()
sse = defaultdict(float)
vp_edge = []
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.VertexSBAPointXYZ()
vp.set_estimate(point + np.random.randn(3))
vp.set_id(point_id)
vp.set_marginalized(True)
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
e = g2o.EdgeSE3ProjectXYZ()
e.set_vertex(0,vp)
e.set_vertex(1,optimizer.vertex(j))
e.set_measurement(z)
e.set_information(np.identity(2))
rk = g2o.RobustKernelHuber()
e.set_robust_kernel(rk)
rk.set_delta(delta)
e.fx = fx
e.fy = fy
e.cx = cx
e.cy = cy
optimizer.add_edge(e)
vp_edge.append(e)
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(optimizer.vertices())
print()
print('Performing full BA:')
optimizer.initialize_optimization()
optimizer.set_verbose(True)
optimizer.optimize(5)
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)))
# print(optimizer.vertices())
print()
for i in xrange(num_pose):
print(optimizer.vertex(i).estimate().inverse().matrix())
j = num_pose
for i in xrange(len(inliers)):
print(optimizer.vertex(j).estimate().shape)
j += 1
i = 0
"""
def bundle_adjustment(keyframes, points, local_window, fixed_points=False, verbose=False, rounds=10, use_robust_kernel=False, abort_flag=g2o.Flag()):
if local_window is None:
local_frames = keyframes
else:
local_frames = keyframes[-local_window:]
# create g2o optimizer
opt = g2o.SparseOptimizer()
block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
#block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3())
solver = g2o.OptimizationAlgorithmLevenberg(block_solver)
opt.set_algorithm(solver)
opt.set_force_stop_flag(abort_flag)
thHuberMono = math.sqrt(5.991); # chi-square 2 DOFS
graph_keyframes, graph_points = {}, {}
# add frame vertices to graph
for kf in (local_frames if fixed_points else keyframes): # if points are fixed then consider just the local frames, otherwise we need all frames or at least two frames for each point
if kf.is_bad:
continue
#print('adding vertex frame ', f.id, ' to graph')
se3 = g2o.SE3Quat(kf.Rcw, kf.tcw)
v_se3 = g2o.VertexSE3Expmap()
v_se3.set_estimate(se3)
v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!)
v_se3.set_fixed(kf.kid==0 or kf not in local_frames) #(use f.kid here!)
opt.add_vertex(v_se3)
# confirm pose correctness
#est = v_se3.estimate()
#assert np.allclose(pose[0:3, 0:3], est.rotation().matrix())
#assert np.allclose(pose[0:3, 3], est.translation())
graph_keyframes[kf] = v_se3
num_edges = 0
# add point vertices to graph
for p in points:
assert(p is not None)
if p.is_bad: # do not consider bad points
continue
if __debug__:
if not any([f in keyframes for f in p.keyframes()]):
Printer.red('point without a viewing frame!!')
continue
#print('adding vertex point ', p.id,' to graph')
v_p = g2o.VertexSBAPointXYZ()
v_p.set_id(p.id * 2 + 1) # odd ids
v_p.set_estimate(p.pt[0:3])
v_p.set_marginalized(True)
v_p.set_fixed(fixed_points)
opt.add_vertex(v_p)
graph_points[p] = v_p
# add edges
for kf, idx in p.observations():
if kf.is_bad:
continue
if kf not in graph_keyframes:
continue
#print('adding edge between point ', p.id,' and frame ', f.id)
edge = g2o.EdgeSE3ProjectXYZ()
edge.set_vertex(0, v_p)
edge.set_vertex(1, graph_keyframes[kf])
edge.set_measurement(kf.kpsu[idx])
invSigma2 = Frame.feature_manager.inv_level_sigmas2[kf.octaves[idx]]
edge.set_information(np.eye(2)*invSigma2)
if use_robust_kernel:
edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono))
edge.fx = kf.camera.fx
edge.fy = kf.camera.fy
edge.cx = kf.camera.cx
edge.cy = kf.camera.cy
opt.add_edge(edge)
num_edges += 1
if verbose:
opt.set_verbose(True)
opt.initialize_optimization()
opt.optimize(rounds)
# put frames back
for kf in graph_keyframes:
est = graph_keyframes[kf].estimate()
#R = est.rotation().matrix()
#t = est.translation()
#f.update_pose(poseRt(R, t))
kf.update_pose(g2o.Isometry3d(est.orientation(), est.position()))
# put points back
if not fixed_points:
for p in graph_points:
p.update_position(np.array(graph_points[p].estimate()))
p.update_normal_and_depth(force=True)
mean_squared_error = opt.active_chi2()/max(num_edges,1)
return mean_squared_error
