def handle_raw_stereo(self, msg):
size = (msg.left_info.width, msg.left_info.height)
if self.vo == None:
cam = camera.StereoCamera(msg.right_info)
self.fd = FeatureDetectorFast(300)
self.ds = DescriptorSchemeCalonder()
self.vo = VisualOdometer(cam, scavenge = False,
inlier_error_threshold = 3.0, sba = None,
inlier_thresh = 100,
position_keypoint_thresh = 0.2, angle_keypoint_thresh = 0.15)
self.vo.num_frames = self.startframe
pair = [Image.fromstring("L", size, i.uint8_data.data) for i in [ msg.left_image, msg.right_image ]]
af = SparseStereoFrame(pair[0], pair[1], feature_detector = self.fd, descriptor_scheme = self.ds)
self.vo.handle_frame(af)
self.frame_timestamps[af.id] = msg.header.stamp
if self.skel.add(self.vo.keyframe):
print "====>", self.vo.keyframe.id, self.frame_timestamps[self.vo.keyframe.id]
#print self.tf.getFrameStrings()
#assert self.tf.frameExists("wide_stereo_optical_frame")
#assert self.tf.frameExists("odom_combined")
N = sorted(self.skel.nodes)
for a,b in zip(N, N[1:]):
if (a,b) in self.skel.edges:
t0 = self.frame_timestamps[a]
t1 = self.frame_timestamps[b]
if self.tf.canTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined"):
t,r = self.tf.lookupTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined")
relpose = Pose()
relpose.fromlist(self.tf.fromTranslationRotation(t,r))
self.wheel_odom_edges.add((a, b, relpose, 1.0))
self.send_map(msg.header.stamp)
def move_forward(i, prev):
""" Forward 1 meter, turn around, Back 1 meter """
if i == 0:
return Pose(rotation(0, 0, 1, 0), (0, 0, 0))
elif i < 10:
return prev * Pose(rotation(0, 0, 1, 0), (0, 0, .1))
elif i < 40:
return prev * Pose(rotation(math.pi / 30, 0, 1, 0), (0, 0, 0))
elif i < 50:
return prev * Pose(rotation(0, 0, 1, 0), (0, 0, .1))
def PE(self, af0, af1):
pairs = [ (a,b) for (a,b,_) in self.ds.match0(self.node_kp[af1], self.node_descriptors[af1], self.node_kp[af0], self.node_descriptors[af0],True) ]
inl,R,T = self.pe.estimateC(self.cam, self.node_kp[af0], self.cam, self.node_kp[af1], pairs)
if inl == 0:
return (0, None)
else:
r33 = numpy.mat(numpy.array(R).reshape(3,3))
return (inl, Pose(r33, numpy.array(T)))
def newpose(self, id):
try:
self.pg.recomputeAllTransformations()
xyz,euler = self.pg.vertex(id)
p = from_xyz_euler(xyz, euler)
except:
print "Failed to get vertex", id
p = Pose()
return p
def getImage(i):
theta = (i / 400.0) * math.pi * 2
wobble = 0.05 * (sin(theta * 9.5) + sin(theta * 3.5))
r = 4.0 + wobble
desired_pose = Pose(rotation(theta, 0, 1, 0),
(4.0 - r * math.cos(theta), 0, r * math.sin(theta)))
if not os.access("/tmp/out%06d.png" % i, os.R_OK):
cam = ray_camera(desired_pose, stereo_cam)
imL = Image.new("RGB", (640, 480))
imR = Image.new("RGB", (640, 480))
imD = [
Image.new("F", (640, 480)),
Image.new("F", (640, 480)),
Image.new("F", (640, 480))
]
def is_visible(p, pt):
camx, camy, camz = (~p).xform(pt.x, pt.y, pt.z)
if camz <= 0:
return False
if abs(camx / camz) > 2.0:
return False
if abs(camy / camz) > 2.0:
return False
return True
scene = [
object(sphere(center, 0.3), shadeLitCloud, {'scale': 10})
for center in blobs if is_visible(desired_pose, center)
]
scene += [
object(isphere(vec3(0, 0, 0), 1000), shadeLitCloud,
{'scale': 0.001})
]
render_stereo_scene(imL, imR, imD, cam, scene, i * 0.033)
imL = imL.convert("L")
imR = imR.convert("L")
im = Image.merge("RGB", (imL, imR, imR))
im.save("/tmp/out%06d.png" % i)
imD[0].save("/tmp/out%06d-x.tiff" % i)
imD[1].save("/tmp/out%06d-y.tiff" % i)
imD[2].save("/tmp/out%06d-z.tiff" % i)
else:
im = Image.open("/tmp/out%06d.png" % i)
imL, imR, _ = im.split()
return (desired_pose, imL, imR)
def test_pe(self):
random.seed(0)
cloud = [(4 * (random.random() - 0.5), 4 * (random.random() - 0.5),
4 * (random.random() - 0.5)) for i in range(20)]
vo = VisualOdometer(
camera.Camera(
(389.0, 389.0, 89.23 * 1e-3, 323.42, 323.42, 274.95)))
stereo_cam = {}
af = {}
fd = FeatureDetectorStar(300)
ds = DescriptorSchemeSAD()
for i in range(5):
stereo_cam[i] = camera.Camera(
(389.0, 389.0, .080 + .010 * i, 323.42, 323.42, 274.95))
desired_pose = Pose()
cam = ray_camera(desired_pose, stereo_cam[i])
imL = Image.new("RGB", (640, 480))
imR = Image.new("RGB", (640, 480))
scene = []
scene += [
object(isphere(vec3(0, 0, 0), 1000), shadeLitCloud,
{'scale': 0.001})
]
scene += [
object(isphere(vec3(x, y, z + 6), 0.3), shadeLitCloud,
{'scale': 3}) for (x, y, z) in cloud
]
imL, imR = [im.convert("L") for im in [imL, imR]]
render_stereo_scene(imL, imR, None, cam, scene, 0)
af[i] = SparseStereoFrame(imL,
imR,
feature_detector=fd,
descriptor_scheme=ds)
vo.process_frame(af[i])
pe = PoseEstimator()
for i1 in range(5):
for i0 in range(5):
pairs = vo.temporal_match(af[i1], af[i0])
res = pe.estimateC(stereo_cam[i0], af[i0].features(),
stereo_cam[i1], af[i1].features(), pairs,
False)
x, y, z = res[2]
self.assertAlmostEqual(x, 0, 0)
self.assertAlmostEqual(y, 0, 0)
self.assertAlmostEqual(z, 0, 0)
def handleFrame(self):
r = False
(w,h,li,ri) = self.source.getImage()
self.f = SparseStereoFrame(li, ri,
disparity_range = 96,
feature_detector = self.fd,
descriptor_scheme = self.ds)
self.vo.handle_frame(self.f)
print self.vo.inl
self.inlier_history = self.inlier_history[-20:] + [self.vo.inl]
# If the VO inlier count falls below 20, we're not well-connected for this link
if self.vo.inl < 20:
self.connected = False
self.f.pose = Pose()
self.snail_trail = []
# Add a frame to graph if:
# Frame zero, or
# There have been N successful preceding frames, and either
# Not connected,
# or if connected, the distance from the youngest graph frame to this frame is > thresh
add_to_graph = None
if self.vo.keyframe.id == 0 and len(self.skel.nodes) == 0:
add_to_graph = "Zero keyframe"
elif min(self.inlier_history) > 20:
if not self.connected:
add_to_graph = "Unconnected - but now recovering good poses"
# This is a new graph, so generate a new label
self.label += 1
else:
relpose = ~self.last_added_pose * self.f.pose
if relpose.distance() > self.skel_dist_thresh:
add_to_graph = "Connected and distance thresh passed"
if add_to_graph:
self.skel.setlabel(self.label)
self.skel.add(self.vo.keyframe, self.connected)
print "ADD %d %s" % (self.f.id, add_to_graph)
self.connected = True
self.last_added_pose = self.f.pose
self.snail_trail = []
# self.ioptimize(10)
self.snail_trail.append(self.f.pose)
# Render the camera view using OpenCV, then load it into an OpenGL texture
if True:
if not self.cvim:
self.cvim = cv.CreateImage((w,h/2), cv.IPL_DEPTH_8U, 3)
im = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
im2 = cv.CreateImage((w/2,h/2), cv.IPL_DEPTH_8U, 1)
for i,src in enumerate([li, ri]):
cv.SetData(im, str(src.tostring()), w)
cv.Resize(im, im2)
cv.SetImageROI(self.cvim, (i * w / 2, 0, w / 2, h / 2))
cv.CvtColor(im2, self.cvim, cv.CV_GRAY2BGR)
cv.ResetImageROI(self.cvim)
def annotate(cvim):
green = cv.RGB(0,255,0)
red = cv.RGB(0,0,255)
def half(p):
return (p[0] / 2, p[1] / 2)
for (x,y,d) in self.f.features():
cv.Circle(cvim, half((x, y)), 2, green)
d = int(d)
cv.Line(cvim, ((w+x-d)/2, y/2 - 2), ((w+x-d)/2, y/2 + 2), green)
a_features = self.vo.keyframe.features()
b_features = self.f.features()
inliers = set([ (b,a) for (a,b) in self.vo.pe.inl])
outliers = set(self.vo.pairs) - inliers
for (a,b) in inliers:
cv.Line(cvim, half(b_features[b]), half(a_features[a]), green)
for (a,b) in outliers:
cv.Line(cvim, half(b_features[b]), half(a_features[a]), red)
annotate(self.cvim)
glBindTexture(GL_TEXTURE_2D, demo.textures[2])
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB8, 640, 240, 0, GL_RGB, GL_UNSIGNED_BYTE, self.cvim.tostring())
if self.camera_preview:
cv.ShowImage("Camera", self.cvim)
if cv.WaitKey(10) == 27:
r = True
return r
def send_map(self, stamp):
if len(self.skel.nodes) < 4:
return
TG = nx.MultiDiGraph()
for i in self.skel.nodes:
TG.add_node(i)
for (a,b,p,c) in self.skel.every_edge + list(self.wheel_odom_edges):
TG.add_edge(a, b, (p, c))
here = max(self.skel.nodes)
# TG is the total graph, G is the local subgraph
uTG = TG.to_undirected()
print "uTG", uTG.nodes(), uTG.edges()
close = set([here])
for i in range(self.optimization_distance):
close |= set(nx.node_boundary(uTG, close))
print "close", close
G = TG.subgraph(close)
uG = uTG.subgraph(close)
pg = TreeOptimizer3()
def mk_covar(xyz, rp, yaw):
return (1.0 / math.sqrt(xyz),1.0 / math.sqrt(xyz), 1.0 / math.sqrt(xyz), 1.0 / math.sqrt(rp), 1.0 / math.sqrt(rp), 1.0 / math.sqrt(yaw))
weak = mk_covar(9e10,3,3)
strong = mk_covar(0.0001, 0.000002, 0.00002)
pg.initializeOnlineOptimization()
def pgadd(p, n, data):
relpose, strength = data
if strength == 0.0:
cov = weak
else:
cov = strong
pg.addIncrementalEdge(p, n, relpose.xform(0,0,0), relpose.euler(), cov)
Gedges = G.edges(data = True)
revmap = {}
map = {}
for n in nx.dfs_preorder(uG, here):
revmap[len(map)] = n
map[n] = len(map)
priors = [p for p in uG.neighbors(n) if p in map]
if priors == []:
print "START NODE", n, "as", map[n]
else:
print "NEXT NODE", n
p = priors[0]
if not G.has_edge(p, n):
n,p = p,n
data = G.get_edge_data(p, n)[0]
Gedges.remove((p, n, data))
pgadd(map[p], map[n], data)
for (p,n,d) in Gedges:
pgadd(map[p], map[n], d)
pg.initializeOnlineIterations()
start_error = pg.error()
for i in range(10):
pg.iterate()
print "Error from", start_error, "to", pg.error()
pg.recomputeAllTransformations()
print self.tf.getFrameStrings()
target_frame = "base_link"
t = self.tf.getLatestCommonTime("wide_stereo_optical_frame", target_frame)
trans,rot = self.tf.lookupTransform(target_frame, "wide_stereo_optical_frame", t)
xp = Pose()
xp.fromlist(self.tf.fromTranslationRotation(trans,rot))
roadmap_nodes = dict([ (n, (30,0,0)) for n in TG.nodes() ])
nl = sorted(roadmap_nodes.keys())
print "nl", nl
for i in sorted(map.values()):
xyz,euler = pg.vertex(i)
p = from_xyz_euler(xyz, euler)
pose_in_target = xp * p
x,y,z = pose_in_target.xform(0,0,0)
euler = pose_in_target.euler()
print x,y,z, euler
roadmap_nodes[revmap[i]] = (x, y, euler[2])
roadmap_edges = []
for (p,n) in set(TG.edges()):
print p,n
best_length = max([ (confidence, pose.distance()) for (pose, confidence) in TG.get_edge_data(p, n).values()])[1]
roadmap_edges.append((p, n, best_length))
msg = vslam.msg.Roadmap()
msg.header.stamp = stamp
msg.header.frame_id = "base_link"
msg.nodes = [vslam.msg.Node(*roadmap_nodes[n]) for n in nl]
print "(p,n)", [ (p,n) for (p,n,l) in roadmap_edges ]
msg.edges = [vslam.msg.Edge(nl.index(p), nl.index(n), l) for (p, n, l) in roadmap_edges]
msg.localization = nl.index(here)
self.pub.publish(msg)
if 1:
import matplotlib.pyplot as plt
fig = plt.figure(figsize = (12, 6))
plt.subplot(121)
pos = nx.spring_layout(TG, iterations=1000)
nx.draw(TG, pos,node_color='#A0CBE2')
nx.draw_networkx_edges(G, pos, with_labels=False, edge_color='r', alpha=0.5, width=25.0, arrows = False)
plt.subplot(122)
nx.draw(G, pos = dict([(n, roadmap_nodes[n][:2]) for n in G.nodes()]))
#plt.show()
plt.savefig("/tmp/map_%d.png" % here)
def add(self, this, connected = 1):
""" add frame *this* to skeleton, *connected* 1 means good link to
previous, 0 means no link to previous - use fill data, -1 means no
link to previous force weak link.
"""
if len(self.nodes) == 0:
self.nodes.add(this.id)
self.node_labels[this.id] = self.label
r = True
elif not(this.id in self.nodes):
previd = max(self.nodes)
# Ignore the node if there are less than node_vist frames since the previous node
# Treat fill data like regular links
if (connected != -1) and (this.id - previd) < self.node_vdist:
return False
if (connected == 1) and self.prev_pose:
print "Strong link from %d to %d" % (previd, this.id)
relpose = ~self.prev_pose * this.pose
inf = strong
print "LINK: Strong from obs", relpose.xform(0,0,0)
self.every_edge.append((previd, this.id, relpose, 1.0))
else:
if self.fills and (connected == 0):
print "Strong link from %d to %d" % (previd, this.id)
relpose = self.gt(self.prev_id, this.id)
self.every_edge.append((previd, this.id, relpose, 1.0))
inf = strong
print "LINK: Strong from fill", relpose.xform(0,0,0)
else:
self.weak_edges.append((previd, this.id))
print "Weak link from %d to %d" % (previd, this.id)
relpose = Pose(numpy.identity(3), [ 0, 0, 0 ])
self.every_edge.append((previd, this.id, relpose, 0.0))
inf = weak
print "LINK: Weak"
self.nodes.add(this.id)
self.node_labels[this.id] = self.label
self.edges.add( (previd, this.id) )
self.timer['toro add'].start()
vtop = self.pg.addIncrementalEdge(previd, this.id, relpose.xform(0,0,0), relpose.euler(), inf)
self.vset.add(previd)
self.vset.add(this.id)
# print self.vset
print "ADDED VO CONSTRAINT", previd, this.id, inf
self.timer['toro add'].stop()
#print "added node at", this.pose.xform(0,0,0), "in graph as", self.newpose(this.id).xform(0,0,0)
self.memoize_node_kp_d(this)
far = [ id for id in self.place_find(this.descriptors()) if (not id in [this.id, previd])]
self.place_ids.append(this.id)
self.add_links(this.id, far)
r = True
else:
r = False
if r:
self.prev_pose = this.pose
self.prev_id = this.id
#self.prev_frame = this
return r
def gt(self, i0, i1):
assert i0 <= i1
r = ~self.fills[i0] * self.fills[i1]
return r
p = Pose()
p.M[0,0] = r.M[0,0]
p.M[0,1] = r.M[0,2]
p.M[0,2] = -r.M[0,1]
p.M[0,3] = r.M[0,3]
p.M[1,0] = -r.M[2,1]
p.M[1,1] = r.M[2,2]
p.M[1,2] = -r.M[2,0]
p.M[1,3] = -r.M[2,3]
p.M[2,0] = -r.M[1,0]
p.M[2,1] = r.M[1,2]
p.M[2,2] = r.M[1,1]
p.M[2,3] = r.M[1,3]
return p
cam90 = Pose(numpy.array([[ 1, 0, 0 ], [ 0, 0, -1 ], [ 0, 1, 0 ]]), numpy.array([0, 0, 0]))
return cam90 * r * ~cam90
def fill(self, filename):
startframe = None
if len(self.nodes) != 0:
my_start = max(self.nodes) + 1
else:
my_start = 0
from tf import transformations
self.fills = {}
for l in open(filename):
f = l.split()
if len(f) == 11:
if not startframe:
startframe = int(f[1])
assert f[4] == 'pose:'
X = float(f[5])
Y = float(f[6])
Z = float(f[7])
if 0:
roll = math.pi * float(f[8]) / 180.0
pitch = math.pi * float(f[9]) / 180.0
yaw = math.pi * float(f[10]) / 180.0
sa = math.sin(yaw);
sb = math.sin(pitch);
sg = math.sin(roll);
ca = math.cos(yaw);
cb = math.cos(pitch);
cg = math.cos(roll);
P = numpy.array(
[ [ ca*cb , -sa*cg + ca*sb*sg , sa*sg + ca*sb*cg ],
[ sa*cb , ca*cg + sa*sb*sg , -ca*sg + sa*sb*cg ],
[ -sb , cb*sg , cb*cg ]]).T
else:
euler = [ float(c) * math.pi / 180 for c in f[8:11] ]
sa = math.sin(euler[2]);
sb = math.sin(euler[1]);
sg = math.sin(euler[0]);
ca = math.cos(euler[2]);
cb = math.cos(euler[1]);
cg = math.cos(euler[0]);
P = numpy.zeros((3,3))
P[0,0] = ca*cb;
P[0,1] = -sa*cg + ca*sb*sg;
P[0,2] = sa*sg + ca*sb*cg;
P[1,0] = sa*cb;
P[1,1] = ca*cg + sa*sb*sg;
P[1,2] = -ca*sg + sa*sb*cg;
P[2,0] = -sb;
P[2,1] = cb*sg;
P[2,2] = cb*cg;
cam90 = Pose(numpy.array([[ 1, 0, 0 ], [ 0, 0, -1 ], [ 0, 1, 0 ]]), numpy.array([0, 0, 0]))
veh_t = Pose(P, numpy.array([X, Y, Z]))
Tc2v = Pose(
numpy.array([
[0.010546 , -0.006048, 0.999926 ],
[-0.999650 , 0.024210 , 0.010689 ],
[-0.024273 ,-0.999689 ,-0.005791 ]]),
numpy.array(
[ 1.783353 , 0.265480 , -0.041039 ]))
self.fills[my_start + int(f[1])-startframe] = (~Tc2v * veh_t) * Tc2v
if 0:
import pylab, sys
po = [ self.fills[x].xform(0,0,0) for x in sorted(self.fills.keys()) ]
pylab.scatter([x for (x,y,z) in po], [y for (x,y,z) in po], c = 'r')
print self.fills[0].M
po = [ self.gt(0, x).xform(0,0,0) for x in sorted(self.fills.keys()) ]
pylab.scatter([x for (x,y,z) in po], [z for (x,y,z) in po], c = 'g')
pylab.show()
sys.exit(1)
def ground_truth(p, q):
return Pose(
transformations.rotation_matrix_from_quaternion(
[q.x, q.y, q.z, q.w])[:3, :3], [p.x, p.y, p.z])