def test_data(self):
"""Test functions in SfmData"""
# Create new track with 3 measurements
i1, i2, i3 = 3, 5, 6
uv_i1 = Point2(21.23, 45.64)
# translating along X-axis
uv_i2 = Point2(45.7, 45.64)
uv_i3 = Point2(68.35, 45.64)
# add measurements and arbitrary point to the track
measurements = [(i1, uv_i1), (i2, uv_i2), (i3, uv_i3)]
pt = Point3(1.0, 6.0, 2.0)
track2 = SfmTrack(pt)
track2.addMeasurement(i1, uv_i1)
track2.addMeasurement(i2, uv_i2)
track2.addMeasurement(i3, uv_i3)
self.data.addTrack(self.tracks)
self.data.addTrack(track2)
# Number of tracks in SfmData is 2
self.assertEqual(self.data.numberTracks(), 2)
# camera idx of first measurement of second track corresponds to i1
cam_idx, img_measurement = self.data.track(1).measurement(0)
self.assertEqual(cam_idx, i1)
def test_values(self):
values = Values()
E = EssentialMatrix(Rot3(), Unit3())
tol = 1e-9
values.insert(0, Point2(0, 0))
values.insert(1, Point3(0, 0, 0))
values.insert(2, Rot2())
values.insert(3, Pose2())
values.insert(4, Rot3())
values.insert(5, Pose3())
values.insert(6, Cal3_S2())
values.insert(7, Cal3DS2())
values.insert(8, Cal3Bundler())
values.insert(9, E)
values.insert(10, imuBias_ConstantBias())
# Special cases for Vectors and Matrices
# Note that gtsam's Eigen Vectors and Matrices requires double-precision
# floating point numbers in column-major (Fortran style) storage order,
# whereas by default, numpy.array is in row-major order and the type is
# in whatever the number input type is, e.g. np.array([1,2,3])
# will have 'int' type.
#
# The wrapper will automatically fix the type and storage order for you,
# but for performance reasons, it's recommended to specify the correct
# type and storage order.
# for vectors, the order is not important, but dtype still is
vec = np.array([1., 2., 3.])
values.insert(11, vec)
mat = np.array([[1., 2.], [3., 4.]], order='F')
values.insert(12, mat)
# Test with dtype int and the default order='C'
# This still works as the wrapper converts to the correct type and order for you
# but is nornally not recommended!
mat2 = np.array([[1, 2, ], [3, 5]])
values.insert(13, mat2)
self.assertTrue(values.atPoint2(0).equals(Point2(0,0), tol))
self.assertTrue(values.atPoint3(1).equals(Point3(0,0,0), tol))
self.assertTrue(values.atRot2(2).equals(Rot2(), tol))
self.assertTrue(values.atPose2(3).equals(Pose2(), tol))
self.assertTrue(values.atRot3(4).equals(Rot3(), tol))
self.assertTrue(values.atPose3(5).equals(Pose3(), tol))
self.assertTrue(values.atCal3_S2(6).equals(Cal3_S2(), tol))
self.assertTrue(values.atCal3DS2(7).equals(Cal3DS2(), tol))
self.assertTrue(values.atCal3Bundler(8).equals(Cal3Bundler(), tol))
self.assertTrue(values.atEssentialMatrix(9).equals(E, tol))
self.assertTrue(values.atimuBias_ConstantBias(
10).equals(imuBias_ConstantBias(), tol))
# special cases for Vector and Matrix:
actualVector = values.atVector(11)
self.assertTrue(np.allclose(vec, actualVector, tol))
actualMatrix = values.atMatrix(12)
self.assertTrue(np.allclose(mat, actualMatrix, tol))
actualMatrix2 = values.atMatrix(13)
self.assertTrue(np.allclose(mat2, actualMatrix2, tol))
def test_optimize(self):
"""Do trivial test with three optimizer variants."""
fg = NonlinearFactorGraph()
model = gtsam.noiseModel.Unit.Create(2)
fg.add(PriorFactorPoint2(KEY1, Point2(0, 0), model))
# test error at minimum
xstar = Point2(0, 0)
optimal_values = Values()
optimal_values.insert(KEY1, xstar)
self.assertEqual(0.0, fg.error(optimal_values), 0.0)
# test error at initial = [(1-cos(3))^2 + (sin(3))^2]*50 =
x0 = Point2(3, 3)
initial_values = Values()
initial_values.insert(KEY1, x0)
self.assertEqual(9.0, fg.error(initial_values), 1e-3)
# optimize parameters
ordering = Ordering()
ordering.push_back(KEY1)
# Gauss-Newton
gnParams = GaussNewtonParams()
gnParams.setOrdering(ordering)
actual1 = GaussNewtonOptimizer(fg, initial_values, gnParams).optimize()
self.assertAlmostEqual(0, fg.error(actual1))
# Levenberg-Marquardt
lmParams = LevenbergMarquardtParams.CeresDefaults()
lmParams.setOrdering(ordering)
actual2 = LevenbergMarquardtOptimizer(fg, initial_values,
lmParams).optimize()
self.assertAlmostEqual(0, fg.error(actual2))
# Levenberg-Marquardt
lmParams = LevenbergMarquardtParams.CeresDefaults()
lmParams.setLinearSolverType("ITERATIVE")
cgParams = PCGSolverParameters()
cgParams.setPreconditionerParams(DummyPreconditionerParameters())
lmParams.setIterativeParams(cgParams)
actual2 = LevenbergMarquardtOptimizer(fg, initial_values,
lmParams).optimize()
self.assertAlmostEqual(0, fg.error(actual2))
# Dogleg
dlParams = DoglegParams()
dlParams.setOrdering(ordering)
actual3 = DoglegOptimizer(fg, initial_values, dlParams).optimize()
self.assertAlmostEqual(0, fg.error(actual3))
# Graduated Non-Convexity (GNC)
gncParams = GncLMParams()
actual4 = GncLMOptimizer(fg, initial_values, gncParams).optimize()
self.assertAlmostEqual(0, fg.error(actual4))
def test_transformFrom(self):
"""Test transformFrom method."""
pose = Pose2(2, 4, -math.pi / 2)
actual = pose.transformFrom(Point2(2, 1))
expected = Point2(3, 2)
self.gtsamAssertEquals(actual, expected, 1e-6)
# multi-point version
points = np.stack([Point2(2, 1), Point2(2, 1)]).T
actual_array = pose.transformFrom(points)
self.assertEqual(actual_array.shape, (2, 2))
expected_array = np.stack([expected, expected]).T
np.testing.assert_allclose(actual_array, expected_array, atol=1e-6)
def create_initial_estimate(self, pose_indices):
"""Create initial estimate from ???."""
wRc = Rot3(1, 0, 0, 0, 0, 1, 0, -1, 0) # camera to world rotation
depth = 20
initial_estimate = gtsam.Values()
# Create dictionary for initial estimation
for img_idx, features in enumerate(self._image_features):
for kp_idx, keypoint in enumerate(features.keypoints):
if self.image_points[img_idx].kp_3d_exist(kp_idx):
landmark_id = self.image_points[img_idx].kp_3d(kp_idx)
# Filter invalid landmarks
if self._landmark_estimates[
landmark_id].seen >= self._min_landmark_seen:
key_point = Point2(keypoint[0], keypoint[1])
key = P(landmark_id)
if not initial_estimate.exists(key):
# do back-projection
pose = Pose3(wRc,
Point3(0, self.delta_z * img_idx, 2))
landmark_3d_point = self.back_projection(
key_point, pose, depth)
initial_estimate.insert(P(landmark_id),
landmark_3d_point)
# Initial estimate for poses
for idx in pose_indices:
pose_i = Pose3(wRc, Point3(0, self.delta_z * idx, 2))
initial_estimate.insert(X(idx), pose_i)
return initial_estimate
def superpoint_generator(self, image):
"""Use superpoint to extract features in the image
Returns:
superpoint - Nx2 (gtsam.Point2) numpy array of 2D point observations.
descriptors - Nx256 numpy array of corresponding unit normalized descriptors.
Refer to /SuperPointPretrainedNetwork/demo_superpoint for more information about the parameters
Output of SuperpointFrontend.run():
corners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T.
desc - 256xN numpy array of corresponding unit normalized descriptors.
heatmap - HxW numpy heatmap in range [0,1] of point confidences.
"""
fe = SuperPointFrontend(weights_path="SuperPointPretrainedNetwork/superpoint_v1.pth",
nms_dist=4,
conf_thresh=0.015,
nn_thresh=0.7,
cuda=False)
superpoints, descriptors, _ = fe.run(image)
# Transform superpoints from 3*N numpy array to N*2 numpy array
superpoints = 4*np.transpose(superpoints[:2, ])
superpoints=self.undistort_points(superpoints)
# Transform descriptors from 256*N numpy array to N*256 numpy array
descriptors = np.transpose(descriptors)
# Transform superpoint into gtsam.Point2
superpoints_reformat = [Point2(point) for point in superpoints]
return Features(np.array(superpoints_reformat), descriptors)
def test_back_projection(self):
"""Test back projection"""
actual = self.back_end.back_projection(
Point2(640, 360), Pose3(Rot3(1, 0, 0, 0, 0, 1, 0, -1, 0),
Point3()), 20)
expected = Point3(0, 20, 0)
self.gtsamAssertEquals(actual, expected)
def test_landmark_association(self):
"""Test landmark association."""
superpoint_features = Features(
[[10, 15], [300, 200], [11, 12], [14, 46]],
[[0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0]])
observed_landmarks = Landmarks(
[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]],
[[0, 0, 1], [1, 0, 0], [0, 2, 2], [0, 1, 0]])
observed_landmarks.load_keypoints([[8, 16], [14, 90], [114, 72],
[300, 200]])
expected_observations = [(Point2(10, 15), Point3(1, 1, 1)),
(Point2(300, 200), Point3(4, 4, 4))]
actual_observations = self.trajectory_estimator.landmark_association(
superpoint_features, observed_landmarks)
for i, observation in enumerate(expected_observations):
self.gtsamAssertEquals(actual_observations[i][0], observation[0])
self.gtsamAssertEquals(actual_observations[i][1], observation[1])
def calculate_vision_area(self):
"""Calculate the vision area of the current pose. """
for y in [0, 2.5, 5, 7.5, 10]:
vision_area = []
point_1 = self.back_project(Point2(0, 0), self.calibration, 10, y)
vision_area.append(point_1)
point_2 = self.back_project(Point2(640, 0), self.calibration, 10,
y)
vision_area.append(point_2)
point_3 = self.back_project(Point2(640, 480), self.calibration, 10,
y)
vision_area.append(point_3)
point_4 = self.back_project(Point2(0, 480), self.calibration, 10,
y)
vision_area.append(point_4)
self.vision_area_list.append(vision_area)
def test_back_projection(self):
"""Test back projection"""
fov, width, height = 60, 1280, 720
calibration = Cal3_S2(fov, width, height)
actual = back_projection(
calibration, Point2(640, 360),
Pose3(Rot3(1, 0, 0, 0, 0, 1, 0, -1, 0), Point3()), 20)
expected = Point3(0, 20, 0)
self.gtsamAssertEquals(actual, expected)
fov, width, height = 60, 640, 480
calibration = Cal3_S2(fov, width, height)
actual = back_projection(
calibration, Point2(320, 240),
Pose3(Rot3(1, 0, 0, 0, 0, 1, 0, -1, 0), Point3()), 20)
expected = Point3(0, 20, 0)
self.gtsamAssertEquals(actual, expected)
def test_back_projection(self):
"""test back projection"""
cal = Cal3DS2(fx=1, fy=1, s=0, u0=320, v0=240, k1=0, k2=0, p1=0, p2=0)
point = Point2(320, 240)
pose = Pose3(Rot3(np.array([[0, 1, 0], [0, 0, -1], [1, 0, 0]]).T),
Point3(0, 2, 1.2))
actual_point = back_projection(cal, point, pose)
actual_point.equals(Point3(10, 2, 1.2), tol=1e-9)
def point(self, point):
""" Calculate the similarity transform of a Point2
Parameters:
point - Point2 object
Returns:
Point2 object
"""
return Point2(self._s * np.dot(self._R.matrix(), point.vector()) +
self._t.vector())
def back_projection(self, key_point = Point2(), pose = Pose3(), depth = 10):
# Normalize input key_point
pn = self.cal.calibrate(key_point)
# Transfer normalized key_point into homogeneous coordinate and scale with depth
ph = Point3(depth*pn.x(),depth*pn.y(),depth)
# Transfer the point into the world coordinate
return pose.transform_from(ph)
def calculate_vision_area(calibration, pose, depth, image_size):
"""Calculate the vision area of the current pose.
Return:
vision_area - [[x,y,z]], back project the four image corners [0,0], [width-1,0],[width-1,height-1],[0,height-1]
"""
vision_area = []
point_1 = back_projection(calibration, Point2(0, 0), pose, depth)
vision_area.append([point_1.x(), point_1.y(), point_1.z()])
point_2 = back_projection(calibration, Point2(image_size[0] - 1, 0), pose,
depth)
vision_area.append([point_2.x(), point_2.y(), point_2.z()])
point_3 = back_projection(calibration,
Point2(image_size[0] - 1, image_size[1] - 1),
pose, depth)
vision_area.append([point_3.x(), point_3.y(), point_3.z()])
point_4 = back_projection(calibration, Point2(0, image_size[1] - 1), pose,
depth)
vision_area.append([point_4.x(), point_4.y(), point_4.z()])
return vision_area
def pose(self, pose):
""" Calculate the similarity transform of a Pose2
Parameters:
pose - Pose2 object
Returns:
Pose2 object
"""
R = self._R.compose(pose.rotation())
translation = self._s * \
self._R.rotate(pose.translation()).vector() + self._t.vector()
return Pose2(R.theta(), Point2(translation))
def get_track_with_duplicate_measurements() -> List[SfmMeasurement]:
"""Generates a track with 2 measurements in an image."""
new_measurements = copy.deepcopy(MEASUREMENTS)
new_measurements.append(
SfmMeasurement(
new_measurements[0].i,
new_measurements[0].uv + Point2(2.0, -3.0),
))
return new_measurements
def test_transform(self):
"""Test similarity transform on poses and points."""
expected_poses, expected_points = self.d_map
sim2 = Similarity2(Rot2(math.radians(90)), Point2(10, 10), 2)
poses, points = self.s_map
for i, point in enumerate(points):
point = sim2.point(point)
self.assert_gtsam_equals(expected_points[i], point)
for i, pose in enumerate(poses):
pose = sim2.pose(pose)
self.assert_gtsam_equals(expected_poses[i], pose)
def setUp(self):
"""Set up the optimization problem and ordering"""
# create graph
self.fg = NonlinearFactorGraph()
model = gtsam.noiseModel.Unit.Create(2)
self.fg.add(PriorFactorPoint2(KEY1, Point2(0, 0), model))
# test error at minimum
xstar = Point2(0, 0)
self.optimal_values = Values()
self.optimal_values.insert(KEY1, xstar)
self.assertEqual(0.0, self.fg.error(self.optimal_values), 0.0)
# test error at initial = [(1-cos(3))^2 + (sin(3))^2]*50 =
x0 = Point2(3, 3)
self.initial_values = Values()
self.initial_values.insert(KEY1, x0)
self.assertEqual(9.0, self.fg.error(self.initial_values), 1e-3)
# optimize parameters
self.ordering = Ordering()
self.ordering.push_back(KEY1)
def robot2world(robotPose, objectPose):
worldPose = Pose2(vector3(0, 0, 0))
rx, ry, rth = robotPose
ox, oy, oth = objectPose
rPos = Pose2(vector3(rx, ry, 0))
rAngle = Pose2(vector3(0, 0, np.radians(rth)))
oPose = Pose2(vector3(ox, oy, np.radians(oth)))
rPose = compose(rPos, rAngle)
newPose = rPose.transformFrom(Point2(ox, oy))
x, y = newPose.x(), newPose.y()
return x, y, (rth - oth) % 360
def back_projection(cal, key_point=Point2(), pose=Pose3(), depth=10, undist=0):
"""Back project a 2d key point to 3d base on depth."""
# Normalize input key_point
if undist == 0:
point = cal.calibrate(key_point, tol=1e-5)
if undist == 1:
point = cal.calibrate(key_point)
# Transfer normalized key_point into homogeneous coordinate and scale with depth
point_3d = Point3(depth*point.x(), depth*point.y(), depth)
# Transfer the point into the world coordinate
return pose.transform_from(point_3d)
def get_track_with_one_outlier() -> List[SfmMeasurement]:
"""Generates a track with outlier measurement."""
# perturb one measurement
idx_to_perturb = 5
perturbed_measurements = copy.deepcopy(MEASUREMENTS)
original_measurement = perturbed_measurements[idx_to_perturb]
perturbed_measurements[idx_to_perturb] = SfmMeasurement(
original_measurement.i,
perturbed_measurements[idx_to_perturb].uv + Point2(20.0, -10.0),
)
return perturbed_measurements
def test_align_case_1(self):
"""Test generating similarity transform with gtsam pose2 align test case 1 - translation only."""
# Create expected sim2
expected_R = Rot2(math.radians(0))
expected_s = 1
expected_t = Point2(10, 10)
# Create source points
s_point1 = Point2(0, 0)
s_point2 = Point2(20, 10)
# Create destination points
d_point1 = Point2(10, 10)
d_point2 = Point2(30, 20)
# Align
point_pairs = [[s_point1, d_point1], [s_point2, d_point2]]
sim2 = Similarity2()
sim2.align(point_pairs)
# Check actual sim2 equals to expected sim2
assert (expected_R.equals(sim2._R, 1e-6))
self.assert_gtsam_equals(sim2._t, expected_t)
self.assertEqual(sim2._s, expected_s)
def test_tracks(self):
"""Test functions in SfmTrack"""
# measurement is of format (camera_idx, imgPoint)
# create arbitrary camera indices for two cameras
i1, i2 = 4, 5
# create arbitrary image measurements for cameras i1 and i2
uv_i1 = Point2(12.6, 82)
# translating point uv_i1 along X-axis
uv_i2 = Point2(24.88, 82)
# add measurements to the track
self.tracks.addMeasurement(i1, uv_i1)
self.tracks.addMeasurement(i2, uv_i2)
# Number of measurements in the track is 2
self.assertEqual(self.tracks.numberMeasurements(), 2)
# camera_idx in the first measurement of the track corresponds to i1
cam_idx, img_measurement = self.tracks.measurement(0)
self.assertEqual(cam_idx, i1)
np.testing.assert_array_almost_equal(Point3(0., 0., 0.),
self.tracks.point3())
def test_align(self):
"""Test generating similarity transform with Point2 pairs."""
# Create expected sim2
expected_R = Rot2(math.radians(90))
expected_s = 2
expected_t = Point2(10, 10)
# Create source points
s_point1 = Point2(0.5, 0.5)
s_point2 = Point2(2, 0.5)
# Create destination points
d_point1 = Point2(9, 11)
d_point2 = Point2(9, 14)
# Align
point_pairs = [[s_point1, d_point1], [s_point2, d_point2]]
sim2 = Similarity2()
sim2.align(point_pairs)
# Check actual sim2 equals to expected sim2
assert (expected_R.equals(sim2._R, 1e-6))
self.assertEqual(sim2._s, expected_s)
self.assert_gtsam_equals(sim2._t, expected_t)
def back_projection(self, key_point=Point2(), pose=Pose3(), depth=20):
"""
Back Projection Function.
Input:
key_point-gtsam.Point2, key point location within the image.
pose-gtsam.Pose3, camera pose in world coordinate.
Output:
gtsam.Pose3, landmark pose in world coordinate.
"""
# Normalize input key_point
pn = self._calibration.calibrate(key_point)
# Transfer normalized key_point into homogeneous coordinate and scale with depth
ph = Point3(depth * pn.x(), depth * pn.y(), depth)
# Transfer the point into the world coordinate
return pose.transformFrom(ph)
def test_align(self) -> None:
"""Ensure estimation of the Pose2 element to align two 2d point clouds succeeds.
Two point clouds represent horseshoe-shapes of the same size, just rotated and translated:
| X---X
| |
| X---X
------------------
|
|
O | O
| | |
O---O
"""
pts_a = [
Point2(1, -3),
Point2(1, -5),
Point2(-1, -5),
Point2(-1, -3),
]
pts_b = [
Point2(3, 1),
Point2(1, 1),
Point2(1, 3),
Point2(3, 3),
]
# fmt: on
ab_pairs = Point2Pairs(list(zip(pts_a, pts_b)))
aTb = Pose2.Align(ab_pairs)
self.assertIsNotNone(aTb)
for pt_a, pt_b in zip(pts_a, pts_b):
pt_a_ = aTb.transformFrom(pt_b)
np.testing.assert_allclose(pt_a, pt_a_)
# Matrix version
A = np.array(pts_a).T
B = np.array(pts_b).T
aTb = Pose2.Align(A, B)
self.assertIsNotNone(aTb)
for pt_a, pt_b in zip(pts_a, pts_b):
pt_a_ = aTb.transformFrom(pt_b)
np.testing.assert_allclose(pt_a, pt_a_)
def landmark_association(self, superpoint_features, observed_landmarks):
""" Associate Superpoint feature points with landmark points by matching all superpoint features with projected features.
Parameters:
superpoint_features - A Features Object
observed_landmarks - A Landmarks Object
Returns:
observations - [(Point2(), Point3())]
"""
if superpoint_features.get_length(
) == 0 or observed_landmarks.get_length() == 0:
print("Input data is empty.")
return [[]]
if self.l2_threshold < 0.0:
raise ValueError('\'nn_thresh\' should be non-negative')
observations = []
for i, projected_point in enumerate(observed_landmarks.keypoints):
nearby_feature_indices = []
min_score = self.l2_threshold
# Calculate the pixels distances between current superpoint and all the points in the map
for j, superpoint in enumerate(superpoint_features.keypoints):
x_diff = abs(superpoint[0] - projected_point[0])
y_diff = abs(superpoint[1] - projected_point[1])
if (x_diff < self.x_distance_thresh
and y_diff < self.y_distance_thresh):
# if((x_diff*x_diff+y_diff*y_diff)**0.5 < 2):
nearby_feature_indices.append(j)
# print("nearby_feature_indices", nearby_feature_indices)
if nearby_feature_indices == []:
continue
for feature_index in nearby_feature_indices:
# Compute L2 distance. Easy since vectors are unit normalized.
dmat = np.dot(
np.array(superpoint_features.descriptors[feature_index]),
np.array(observed_landmarks.descriptors[i]).T)
dmat = np.sqrt(2 - 2 * np.clip(dmat, -1, 1))
# Select the minimal L2 distance point
if dmat < min_score:
min_score = dmat
key_point = superpoint_features.keypoints[feature_index]
landmark = observed_landmarks.landmarks[i]
observations.append((Point2(key_point[0], key_point[1]),
Point3(landmark[0], landmark[1],
landmark[2])))
return observations
def bundle_adjustment(self):
"""
Bundle Adjustment.
Input:
self._image_features
self._image_points
Output:
result - gtsam optimzation result
"""
# Initialize factor Graph
graph = gtsam.NonlinearFactorGraph()
pose_indices, point_indices = self.create_index_sets()
initial_estimate = self.create_initial_estimate(pose_indices)
# Create Projection Factor
sigma = 1.0
measurementNoise = gtsam.noiseModel_Isotropic.Sigma(2, sigma)
for img_idx, features in enumerate(self._image_features):
for kp_idx, keypoint in enumerate(features.keypoints):
if self.image_points[img_idx].kp_3d_exist(kp_idx):
landmark_id = self.image_points[img_idx].kp_3d(kp_idx)
if self._landmark_estimates[
landmark_id].seen >= self._min_landmark_seen:
key_point = Point2(keypoint[0], keypoint[1])
graph.add(
gtsam.GenericProjectionFactorCal3_S2(
key_point, measurementNoise, X(img_idx),
P(landmark_id), self._calibration))
# Create priors for first two poses
s = np.radians(60)
poseNoiseSigmas = np.array([s, s, s, 1, 1, 1])
posePriorNoise = gtsam.noiseModel_Diagonal.Sigmas(poseNoiseSigmas)
for idx in (0, 1):
pose_i = initial_estimate.atPose3(X(idx))
graph.add(gtsam.PriorFactorPose3(X(idx), pose_i, posePriorNoise))
# Optimization
optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate)
sfm_result = optimizer.optimize()
return sfm_result, pose_indices, point_indices
def world2robot(robotPose: tuple, objectPose: tuple) -> tuple:
#transforms objectPose from world frame to robot frame
#objectPose: pose of object in robot pose
#robotPose: pose of robot base in world frame
rx, ry, rth = robotPose
ox, oy, oth = objectPose
rPos = Pose2(vector3(
rx,
ry,
))
rAngle = Pose2(vector3(0, 0, np.radians(rth)))
oPose = Pose2(vector3(ox, oy, np.radians(oth)))
rPose = compose(rPos, rAngle)
newPose = rPose.transformTo(Point2(ox, oy))
x, y = newPose.x(), newPose.y()
print(newPose)
return x, y, (oth - rth) % 360
def test_align(self) -> None:
"""Ensure estimation of the Pose2 element to align two 2d point clouds succeeds.
Two point clouds represent horseshoe-shapes of the same size, just rotated and translated:
| X---X
| |
| X---X
------------------
|
|
O | O
| | |
O---O
"""
pts_a = [
Point2(3, 1),
Point2(1, 1),
Point2(1, 3),
Point2(3, 3),
]
pts_b = [
Point2(1, -3),
Point2(1, -5),
Point2(-1, -5),
Point2(-1, -3),
]
# fmt: on
ab_pairs = Point2Pairs(list(zip(pts_a, pts_b)))
bTa = gtsam.align(ab_pairs)
aTb = bTa.inverse()
assert aTb is not None
for pt_a, pt_b in zip(pts_a, pts_b):
pt_a_ = aTb.transformFrom(pt_b)
assert np.allclose(pt_a, pt_a_)
