def fitRunningBoardAtFeet(self):
# get stance frame
startPose = self.getPlanningStartPose()
stanceFrame = self.robotSystem.footstepsDriver.getFeetMidPoint(self.robotSystem.robotStateModel, useWorldZ=False)
stanceFrameAxes = transformUtils.getAxesFromTransform(stanceFrame)
# get pointcloud and extract search region covering the running board
polyData = segmentation.getCurrentRevolutionData()
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.01)
_, polyData = segmentation.removeGround(polyData)
polyData = segmentation.cropToBox(polyData, stanceFrame, [1.0, 1.0, 0.1])
if not polyData.GetNumberOfPoints():
print 'empty search region point cloud'
return
vis.updatePolyData(polyData, 'running board search points', parent=segmentation.getDebugFolder(), color=[0,1,0], visible=False)
# extract maximal points along the stance x axis
perpAxis = stanceFrameAxes[0]
edgeAxis = stanceFrameAxes[1]
edgePoints = segmentation.computeEdge(polyData, edgeAxis, perpAxis)
edgePoints = vnp.getVtkPolyDataFromNumpyPoints(edgePoints)
vis.updatePolyData(edgePoints, 'edge points', parent=segmentation.getDebugFolder(), visible=True)
# ransac fit a line to the edge points
linePoint, lineDirection, fitPoints = segmentation.applyLineFit(edgePoints)
if np.dot(lineDirection, stanceFrameAxes[1]) < 0:
lineDirection = -lineDirection
linePoints = segmentation.thresholdPoints(fitPoints, 'ransac_labels', [1.0, 1.0])
dists = np.dot(vnp.getNumpyFromVtk(linePoints, 'Points')-linePoint, lineDirection)
p1 = linePoint + lineDirection*np.min(dists)
p2 = linePoint + lineDirection*np.max(dists)
vis.updatePolyData(fitPoints, 'line fit points', parent=segmentation.getDebugFolder(), colorByName='ransac_labels', visible=False)
# compute a new frame that is in plane with the stance frame
# and matches the orientation and position of the detected edge
origin = np.array(stanceFrame.GetPosition())
normal = np.array(stanceFrameAxes[2])
# project stance origin to edge, then back to foot frame
originProjectedToEdge = linePoint + lineDirection*np.dot(origin - linePoint, lineDirection)
originProjectedToPlane = segmentation.projectPointToPlane(originProjectedToEdge, origin, normal)
zaxis = np.array(stanceFrameAxes[2])
yaxis = np.array(lineDirection)
xaxis = np.cross(yaxis, zaxis)
xaxis /= np.linalg.norm(xaxis)
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
d = DebugData()
d.addSphere(p1, radius=0.005)
d.addSphere(p2, radius=0.005)
d.addLine(p1, p2)
d.addSphere(originProjectedToEdge, radius=0.001, color=[1,0,0])
d.addSphere(originProjectedToPlane, radius=0.001, color=[0,1,0])
d.addLine(originProjectedToPlane, origin, color=[0,1,0])
d.addLine(originProjectedToEdge, origin, color=[1,0,0])
vis.updatePolyData(d.getPolyData(), 'running board edge', parent=segmentation.getDebugFolder(), colorByName='RGB255', visible=False)
# update the running board box affordance position and orientation to
# fit the detected edge
box = self.spawnRunningBoardAffordance()
boxDimensions = box.getProperty('Dimensions')
t = transformUtils.getTransformFromAxesAndOrigin(xaxis, yaxis, zaxis, originProjectedToPlane)
t.PreMultiply()
t.Translate(-boxDimensions[0]/2.0, 0.0, -boxDimensions[2]/2.0)
box.getChildFrame().copyFrame(t)
self.initialize()
def updateFit(self):
# get and display: .1sec
polyData = segmentation.getDisparityPointCloud()
if (polyData is None):
return
updatePolyData(polyData, 'pointcloud snapshot', colorByName='rgb_colors', visible=False)
t0 = time.time()
if (self.tableCentroid is None):
# initial fit .75 sec
print "Boot Strapping tracker"
self.tableCentroid = segmentation.findAndFitDrillBarrel(polyData)
else:
# refit .07 sec
#print "current centroid"
#print self.tableCentroid
viewFrame = segmentationroutines.SegmentationContext.getGlobalInstance().getViewFrame()
forwardDirection = np.array([1.0, 0.0, 0.0])
viewFrame.TransformVector(forwardDirection, forwardDirection)
robotOrigin = viewFrame.GetPosition()
robotForward =forwardDirection
fitResults = []
drillFrame = segmentation.segmentDrillBarrelFrame(self.tableCentroid, polyData, robotForward)
clusterObj = updatePolyData(polyData, 'surface cluster refit', color=[1,1,0], parent=segmentation.getDebugFolder(), visible=False)
fitResults.append((clusterObj, drillFrame))
segmentation.sortFittedDrills(fitResults, robotOrigin, robotForward)
def fitRunningBoardAtFeet(self):
# get stance frame
startPose = self.getPlanningStartPose()
stanceFrame = self.robotSystem.footstepsDriver.getFeetMidPoint(
self.robotSystem.robotStateModel, useWorldZ=False)
stanceFrameAxes = transformUtils.getAxesFromTransform(stanceFrame)
# get pointcloud and extract search region covering the running board
polyData = segmentation.getCurrentRevolutionData()
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.01)
_, polyData = segmentation.removeGround(polyData)
polyData = segmentation.cropToBox(polyData, stanceFrame,
[1.0, 1.0, 0.1])
if not polyData.GetNumberOfPoints():
print 'empty search region point cloud'
return
vis.updatePolyData(polyData,
'running board search points',
parent=segmentation.getDebugFolder(),
color=[0, 1, 0],
visible=False)
# extract maximal points along the stance x axis
perpAxis = stanceFrameAxes[0]
edgeAxis = stanceFrameAxes[1]
edgePoints = segmentation.computeEdge(polyData, edgeAxis, perpAxis)
edgePoints = vnp.getVtkPolyDataFromNumpyPoints(edgePoints)
vis.updatePolyData(edgePoints,
'edge points',
parent=segmentation.getDebugFolder(),
visible=True)
# ransac fit a line to the edge points
linePoint, lineDirection, fitPoints = segmentation.applyLineFit(
edgePoints)
if np.dot(lineDirection, stanceFrameAxes[1]) < 0:
lineDirection = -lineDirection
linePoints = segmentation.thresholdPoints(fitPoints, 'ransac_labels',
[1.0, 1.0])
dists = np.dot(
vnp.getNumpyFromVtk(linePoints, 'Points') - linePoint,
lineDirection)
p1 = linePoint + lineDirection * np.min(dists)
p2 = linePoint + lineDirection * np.max(dists)
vis.updatePolyData(fitPoints,
'line fit points',
parent=segmentation.getDebugFolder(),
colorByName='ransac_labels',
visible=False)
# compute a new frame that is in plane with the stance frame
# and matches the orientation and position of the detected edge
origin = np.array(stanceFrame.GetPosition())
normal = np.array(stanceFrameAxes[2])
# project stance origin to edge, then back to foot frame
originProjectedToEdge = linePoint + lineDirection * np.dot(
origin - linePoint, lineDirection)
originProjectedToPlane = segmentation.projectPointToPlane(
originProjectedToEdge, origin, normal)
zaxis = np.array(stanceFrameAxes[2])
yaxis = np.array(lineDirection)
xaxis = np.cross(yaxis, zaxis)
xaxis /= np.linalg.norm(xaxis)
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
d = DebugData()
d.addSphere(p1, radius=0.005)
d.addSphere(p2, radius=0.005)
d.addLine(p1, p2)
d.addSphere(originProjectedToEdge, radius=0.001, color=[1, 0, 0])
d.addSphere(originProjectedToPlane, radius=0.001, color=[0, 1, 0])
d.addLine(originProjectedToPlane, origin, color=[0, 1, 0])
d.addLine(originProjectedToEdge, origin, color=[1, 0, 0])
vis.updatePolyData(d.getPolyData(),
'running board edge',
parent=segmentation.getDebugFolder(),
colorByName='RGB255',
visible=False)
# update the running board box affordance position and orientation to
# fit the detected edge
box = self.spawnRunningBoardAffordance()
boxDimensions = box.getProperty('Dimensions')
t = transformUtils.getTransformFromAxesAndOrigin(
xaxis, yaxis, zaxis, originProjectedToPlane)
t.PreMultiply()
t.Translate(-boxDimensions[0] / 2.0, 0.0, -boxDimensions[2] / 2.0)
box.getChildFrame().copyFrame(t)
self.initialize()