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HandEye.lua
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HandEye.lua
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--[[
HandEye.lua
Copyright (c) 2018, Xamla and/or its affiliates. All rights reserved.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
--]]
-- Hand-Pattern or Hand-Eye calibration, depending on if we have an
-- extern or an onboard camera setup.
-- Tested with UR5 and SDA10D.
--package.path = package.path .. ";../../lua/auto_calibration/?.lua"
--package.path = package.path .. ";/home/xamla/Rosvita.Control/lua/auto_calibration/?.lua"
--local calib = require 'handEyeCalibration'
local motionLibrary = require 'xamlamoveit.motionLibrary'
local xutils = require 'xamlamoveit.xutils'
local datatypes = require 'xamlamoveit.datatypes'
local rosvita = require 'xamlamoveit.rosvita'
local cv = require "cv"
require "cv.highgui"
require "cv.videoio"
require "cv.imgproc"
require "cv.calib3d"
require "cv.imgcodecs"
require "cv.features2d"
require 'image'
require 'ximea.ros.XimeaClient'
--require 'GenICamCameraClient'
--require "multiPattern.PatternLocalisation"
local ros = require 'ros'
local tf = ros.tf
local M_PI = 3.14159265359
--local handEye = {}
local autocal = require 'auto_calibration.env'
local CalibrationMode = autocal.CalibrationMode
local HandEye = torch.class('autoCalibration.HandEye', autocal)
local function printf(...)
print(string.format(...))
end
function HandEye:__init(configuration, calibration_folder_name, move_group, motion_service, camera_client, gripper, xamla_mg)
self.configuration = configuration
self.move_group = move_group
self.camera_client = camera_client
self.xamla_mg = xamla_mg
self.motion_service = motion_service
self.world_view_client = rosvita.WorldViewClient.new(motion_service.node_handle)
self.move_groups = self.motion_service:getMoveGroup() -- If no name is specified first move group is used (and this is linked to all endeffectors)
self.gripper = gripper
self.calibration_folder_name = calibration_folder_name
--[[
-- current folder; contains links to the used calibration files
self.current_path = path.join(configuration.output_directory, 'current')
local left_camera = self.configuration.cameras[self.configuration.left_camera_id]
local right_camera = self.configuration.cameras[self.configuration.right_camera_id]
local mode = self.configuration.calibration_mode
if mode == CalibrationMode.SingleCamera then
-- assemble the calibration file name based on the serial of the camera
if left_camera ~= nil then
self.left_camera_serial = left_camera.serial
self.calibration_fn_left = string.format('cam_%s.t7', self.left_camera_serial)
self.calibration_path_left = path.join(self.current_path, self.calibration_fn_left)
end
if right_camera ~= nil then
self.right_camera_serial = right_camera.serial
self.calibration_fn_right = string.format('cam_%s.t7', self.right_camera_serial)
self.calibration_path_right = path.join(self.current_path, self.calibration_fn_right)
end
elseif mode == CalibrationMode.StereoRig then
-- assemble the stereo calibration file name based on the serials of the cameras
if left_camera ~= nil and right_camera ~= nil then
self.left_camera_serial = left_camera.serial
self.right_camera_serial = right_camera.serial
end
self.calibration_fn = string.format('stereo_cams_%s_%s.t7', self.left_camera_serial, self.right_camera_serial)
self.stereo_calibration_path = path.join(self.current_path, self.calibration_fn)
self:loadStereoCalibration(self.stereo_calibration_path)
end
]]
self.tcp_frame_of_reference, self.tcp_end_effector_name = self:getEndEffectorName()
end
function HandEye:loadStereoCalibration(stereo_calib_fn)
-- check first if there is an existing stereo calibration file
if path.exists(stereo_calib_fn) then
local stereoCalib = torch.load(stereo_calib_fn)
self.stereoCalibration = stereoCalib
self.leftCameraMatrix = stereoCalib.camLeftMatrix
self.rightCameraMatrix = stereoCalib.camRightMatrix
self.leftDistCoeffs = stereoCalib.camLeftDistCoeffs
self.rightDistCoeffs = stereoCalib.camRightDistCoeffs
self.rightLeftCamTrafo = stereoCalib.trafoLeftToRightCam
print("self.rightLeftCamTrafo:")
print(self.rightLeftCamTrafo)
print('read stereo calibration file '..stereo_calib_fn)
return true
else
print('Calibration file '..stereo_calib_fn..' does not exist.')
print('Please calibrate cameras first and don\'t forget to save calibration.')
return false
end
end
function HandEye:loadCalibration(calib_fn)
-- check first if there is an existing calibration file
if path.exists(calib_fn) then
local calib = torch.load(calib_fn)
self.calibration = calib
self.cameraMatrix = calib.camMatrix
self.distCoeffs = calib.distCoeffs
print('read calibration file '..calib_fn)
return true
else
print('Calibration file '..calib_fn..' does not exist.')
print('Please calibrate camera first and don\'t forget to save calibration.')
return false
end
end
function HandEye:getEndEffectorName()
local move_group_names, move_group_details = self.move_group.motion_service:queryAvailableMoveGroups()
-- find out the index of the selected move_group
local index = 1
for i = 1, #move_group_names do
if move_group_names[i] == self.configuration.move_group_name then
index = i
printf("Move group: %s (with index: %d)", self.configuration.move_group_name, index)
end
end
local tcp_frame_of_reference = move_group_details[move_group_names[index]].end_effector_link_names[1]
local tcp_end_effector_name = move_group_details[move_group_names[index]].end_effector_names[1]
return tcp_frame_of_reference, tcp_end_effector_name
end
local function createPatternLocalizer(self)
local pattern_geometry = self.configuration.circle_pattern_geometry
local pattern_localizer = autocal.PatternLocalisation()
pattern_localizer.circleFinderParams.minArea = 300
pattern_localizer.circleFinderParams.maxArea = 4000
pattern_localizer:setPatternIDdictionary(torch.load("/home/xamla/Rosvita.Control/lua/auto_calibration/patternIDdictionary.t7"))
pattern_localizer:setDBScanParams(100, 10)
pattern_localizer.debugParams = { circleSearch = false, clustering = false, pose = false }
pattern_localizer:setPatternData(pattern_geometry[2], pattern_geometry[1], pattern_geometry[3])
local mode = self.configuration.calibration_mode
if mode == CalibrationMode.SingleCamera then
pattern_localizer:setCamIntrinsics(self.calibration)
elseif mode == CalibrationMode.StereoRig then
pattern_localizer:setStereoCalibration(self.stereoCalibration)
end
self.pattern_localizer = pattern_localizer
end
function HandEye:captureImageNoWait(camera_configuration)
local camera_serial = camera_configuration.serial
local exposure = camera_configuration.exposure
print("HandEye:captureImageNoWait: camera serial:")
print(camera_serial)
-- capture image
if next(self.camera_client) ~= nil then
self.camera_client:setExposure(exposure, {camera_serial})
else
print('No camera connected! -> No image capturing possible!')
return nil
end
local image = self.camera_client:getImages({camera_serial})
if image:nDimension() > 2 then
image = cv.cvtColor{image, nil, cv.COLOR_RGB2BGR}
end
if image:nDimension() == 2 then
image = cv.cvtColor{image, nil, cv.COLOR_GRAY2BGR}
end
return image
end
local function printPatternPoints(points3d, pattern_height, pattern_width, color, inputImg)
local imgShow
if inputImg ~= nil then
imgShow = inputImg
else
imgShow = cv.cvtColor {src = blackImg, code = cv.COLOR_GRAY2RGB} -- grayToRGB(blackImg) -- create blackImg
end
--cv.drawChessboardCorners {image = imgShow, patternSize = { height = pattern_height, width = pattern_width }, corners = points3d, patternWasFound = true}
local circleScale = 16
local shiftBits = 4
for i=1,points3d:size(1) do
cv.circle {
img = imgShow,
center = {x = points3d[i][1][1] * circleScale, y = points3d[i][1][2] * circleScale},
radius = 300.0,
color = color,
thickness = 2,
lineType = cv.LINE_AA,
shift = shiftBits
}
end
return imgShow
end
local function transformMatrixToQuaternion(rot)
local sqrt = math.sqrt
local trace = rot[1][1] + rot[2][2] + rot[3][3]
local _next = { 2, 3, 1 }
local q = torch.zeros(4)
if trace > 0 then
local r = sqrt(trace + 1)
local s = 0.5 / r
q[1] = 0.5 * r
q[2] = (rot[3][2] - rot[2][3]) * s
q[3] = (rot[1][3] - rot[3][1]) * s
q[4] = (rot[2][1] - rot[1][2]) * s
else
local i = 1
if rot[2][2] > rot[1][1] then
i = 2
end
if rot[3][3] > rot[i][i] then
i = 3
end
local j = _next[i]
local k = _next[j]
local t = rot[i][i] - rot[j][j] - rot[k][k] + 1
local r = sqrt(t)
local s = 0.5 / sqrt(t)
local w = (rot[k][j] - rot[j][k]) * s
q[1] = w
q[i+1] = 0.5 * r
q[j+1] = (rot[j][i] + rot[i][j]) * s
q[k+1] = (rot[k][i] + rot[i][k]) * s
end
return q/q:norm()
end
local function transformQuaternionToMatrix(q)
local w = q[1]
local x = q[2]
local y = q[3]
local z = q[4]
local result = torch.DoubleTensor(3,3)
result[1][1] = 1 - 2*y*y - 2*z*z
result[1][2] = 2*x*y - 2*w*z
result[1][3] = 2*x*z + 2*w*y
result[2][1] = 2*x*y + 2*w*z
result[2][2] = 1 - 2*x*x - 2*z*z
result[2][3] = 2*y*z - 2*w*x
result[3][1] = 2*x*z - 2*w*y
result[3][2] = 2*y*z + 2*w*x
result[3][3] = 1 - 2*x*x - 2*y*y
return result
end
function calc_avg_leftCamBase(H, Hg, Hc)
local Q = torch.DoubleTensor(#Hg, 4)
local avg_pos = torch.zeros(3)
for i = 1,#Hg do
local leftCamPoseInBaseCoords = Hg[i] * H * Hc[i]
avg_pos = avg_pos + leftCamPoseInBaseCoords[{{1,3},{4}}]
local q = transformMatrixToQuaternion(leftCamPoseInBaseCoords[{{1,3},{1,3}}])
Q[i] = q
end
avg_pos = avg_pos / #Hg
local QtQ = Q:t() * Q
local e, V = torch.symeig(QtQ, 'V')
local maxEigenvalue, maxEig_index = torch.max(e,1)
local avg_q = V:t()[maxEig_index[1]]
local avg_rot = transformQuaternionToMatrix(avg_q)
local avg_LeftCamPose = torch.DoubleTensor(4,4)
avg_LeftCamPose[{{1,3},{1,3}}] = avg_rot
avg_LeftCamPose[{{1,3},{4}}] = avg_pos
avg_LeftCamPose[4][4] = 1.0
return avg_LeftCamPose
end
-- calculate magnitude of rotation angle [in radians]; range: [0,pi]
function distanceQ6(q1,q2)
return 2.0 * math.acos(math.abs(q1[1]*q2[1] + q1[2]*q2[2] + q1[3]*q2[3] + q1[4]*q2[4]))
end
-- RANSAC outlier removal for hand-eye calibration:
function ransac_hand_eye(Hc, Hg, min_samples, max_trials, rot_thres, trans_thres)
local min_samples = min_samples or 5
local max_trials = max_trials or 10
local rot_thres = rot_thres or (1.0 * M_PI / 180.0) -- 1°
local trans_thres = trans_thres or 0.001 -- 1mm
local t = torch.Tensor(min_samples)
local best_hand_eye = torch.eye(4)
local best_idx_inliers = {}
local best_Hc_inliers = {}
local best_Hg_inliers = {}
local best_inlier_num = 0
for i = 1, max_trials do
local idx_inliers = {}
local Hc_inliers = {}
local Hg_inliers = {}
-- First take only "min_samples" many, randomly chosen samples to create
-- an initial guess of the hand-eye calibration.
t:random(1, #Hg) -- min_samples many random numbers from 1 to #Hg
print("Indices of randomly chosen initial set:")
print(t)
local Hg_init = {}
local Hc_init = {}
for j = 1, min_samples do
table.insert(Hc_init, Hc[ t[j] ])
table.insert(Hg_init, Hg[ t[j] ])
end
--print('#Hg_init='..#Hg_init..' #Hc_init='..#Hc_init)
local H_init, _, _ = calib.calibrate(Hg_init, Hc_init)
print("Initial H:")
print(H_init)
-- Then repeatedly take the next sample to improve the hand-eye calibration.
-- Note: The next sample is only taken into account, if the resulting hand-eye-matrix does not change too much.
-- Thus, outliers are automatically removed.
local Hc_next = {table.unpack(Hc_init)}
local Hg_next = {table.unpack(Hg_init)}
local H_next = H_init:clone()
local H_next_q = transformMatrixToQuaternion(H_next[{{1,3},{1,3}}])
for j = 1, #Hc do
local Hc_next_backup = {table.unpack(Hc_next)}
local Hg_next_backup = {table.unpack(Hg_next)}
local H_next_backup = H_next:clone()
local H_next_q_backup = H_next_q:clone()
table.insert(Hc_next, Hc[j])
table.insert(Hg_next, Hg[j])
--print('#Hg_next='..#Hg_next..' #Hc_next='..#Hc_next)
local H_next, _, _ = calib.calibrate(Hg_next, Hc_next)
-- Check if hand-eye does not change too much (-> outlier removal)
local H_next_q = transformMatrixToQuaternion(H_next[{{1,3},{1,3}}])
local rotation_difference = distanceQ6(H_next_q_backup, H_next_q)
--print("Rotation difference [in degree]: ", (rotation_difference * 180.0/M_PI))
local translation_difference = (H_next_backup[{{1,3},{4}}] - H_next[{{1,3},{4}}]):norm()
--print("Translation difference [in m]: ", translation_difference)
if rotation_difference < rot_thres and translation_difference < trans_thres then
--print("Successful refinement step.")
table.insert(idx_inliers, j)
else
--print("Unsuccessful refinement step -> take backup.")
Hc_next = {table.unpack(Hc_next_backup)}
Hg_next = {table.unpack(Hg_next_backup)}
H_next = H_next_backup:clone()
H_next_q = H_next_q_backup:clone()
end
end
if #idx_inliers > best_inlier_num then -- the initial sample set with the largest number of inliers is chosen
best_inlier_num = #idx_inliers
best_idx_inliers = idx_inliers
best_Hc_inliers = {table.unpack(Hc_next)}
best_Hg_inliers = {table.unpack(Hg_next)}
best_hand_eye = H_next:clone()
end
print("******************************")
print("Currently best_inlier_num:")
print(best_inlier_num)
print("Currently best_hand_eye:")
print(best_hand_eye)
print("******************************")
end
print("******************************")
print("Final best_inlier_num:")
print(best_inlier_num)
print("Final best_hand_eye:")
print(best_hand_eye)
print("******************************")
return best_Hc_inliers, best_Hg_inliers, best_hand_eye
end
-- Note: This hand-eye calibration can only be used with a stereo camera setup!
-- params: imgData = {imgDataLeft = {imagePaths= {}}, imgDataRight = {imagePaths= {}}}
-- output: Returns the transformation camera_to_tcp or pattern_to_tcp, depending on if we have
-- an 'onboard' camera setup or an 'extern' camera setup
function HandEye:calibrate(imgData, camera_calibration_path, ransac_outlier_removal)
ransac_outlier_removal = ransac_outlier_removal or false
--first load the latest calibration file
local mode = self.configuration.calibration_mode
local left_camera = self.configuration.cameras[self.configuration.left_camera_id]
local right_camera = self.configuration.cameras[self.configuration.right_camera_id]
local success = false
if mode == CalibrationMode.SingleCamera then
-- assemble the calibration file name based on the serial of the camera
if left_camera ~= nil and imgData.imgDataLeft ~= nil then
self.left_camera_serial = left_camera.serial
self.calibration_fn_left = string.format('cam_%s.t7', self.left_camera_serial)
self.calibration_path_left = path.join(camera_calibration_path, self.calibration_fn_left)
print('HandEye:calibrate loading calibration file: '..self.calibration_path_left)
success = self:loadCalibration(self.calibration_path_left)
elseif right_camera ~= nil and imgData.imgDataRight ~= nil then
self.right_camera_serial = right_camera.serial
self.calibration_fn_right = string.format('cam_%s.t7', self.right_camera_serial)
self.calibration_path_right = path.join(camera_calibration_path, self.calibration_fn_right)
print('HandEye:calibrate loading calibration file: '..self.calibration_path_right)
success = self:loadCalibration(self.calibration_path_right)
end
elseif mode == CalibrationMode.StereoRig then
-- assemble the stereo calibration file name based on the serials of the cameras
if left_camera ~= nil and right_camera ~= nil then
self.left_camera_serial = left_camera.serial
self.right_camera_serial = right_camera.serial
end
self.calibration_fn = string.format('stereo_cams_%s_%s.t7', self.left_camera_serial, self.right_camera_serial)
self.stereo_calibration_path = path.join(camera_calibration_path, self.calibration_fn)
print('HandEye:calibrate loading calibration file: '..self.stereo_calibration_path)
success = self:loadStereoCalibration(self.stereo_calibration_path)
end
if success == false then
return
end
local output_path = path.join(self.configuration.output_directory, self.calibration_folder_name)
-- the <calibration_name> folder has to exist or be created to be able to store the hand eye matrices
os.execute('mkdir -p ' .. output_path)
-- load calibration images and TCP data
local imgDataLeft = imgData.imgDataLeft
local imgDataRight = imgData.imgDataRight
local imgDataSingle = imgDataLeft
if imgDataLeft == nil then
imgDataSingle = imgDataRight
end
local Hg = {}
local Hc = {}
-- extract pattern points:
createPatternLocalizer(self)
if self.configuration.debug_output == nil then
self.configuration.debug_output = false
end
local imagesTakenForHandPatternCalib = {}
if mode == CalibrationMode.StereoRig then
local imgGetSize = cv.imread {imgDataLeft.imagePaths[1]}
local imgShowLeft = torch.ByteTensor(imgGetSize:size(1), imgGetSize:size(2), 3)
local imgShowRight = torch.ByteTensor(imgGetSize:size(1), imgGetSize:size(2), 3)
local color = {0, 255, 255}
for i, fn in ipairs(imgDataLeft.imagePaths) do
local fnLeft = imgDataLeft.imagePaths[i]
local fnRight = imgDataRight.imagePaths[i]
local imgLeft = cv.imread {fnLeft}
local imgRight = cv.imread {fnRight}
local robotPose = imgData.jsposes.recorded_poses[i]
local ok, patternPoseRelToCamera, circlesGridPointsLeft, circlesGridPointsRight = self.pattern_localizer:calcCamPoseViaPlaneFit(imgLeft, imgRight, 'left', self.configuration.debug_output, nil, self.configuration.circle_pattern_id)
if ok then
local cameraPoseRelToPattern = torch.inverse(patternPoseRelToCamera)
local cameraPatternTrafo = cameraPoseRelToPattern -- This is used for an extern camera setup.
if self.configuration.camera_location_mode == 'onboard' then
-- We have an onboard camera setup, thus we have to use patternPoseRelToCamera.
cameraPatternTrafo = patternPoseRelToCamera
end
table.insert(Hc, cameraPatternTrafo)
table.insert(Hg, robotPose)
table.insert(imagesTakenForHandPatternCalib, i)
--if self.configuration.debug_output then
if i < #self.pattern_localizer.colorTab then
color = self.pattern_localizer.colorTab[i]
end
imgShowLeft = printPatternPoints(circlesGridPointsLeft, self.pattern_localizer.pattern.height, self.pattern_localizer.pattern.width, color, imgShowLeft)
imgShowRight = printPatternPoints(circlesGridPointsRight, self.pattern_localizer.pattern.height, self.pattern_localizer.pattern.width, color, imgShowRight)
--end
end
end
--if self.configuration.debug_output then
imgShowLeft = cv.resize {imgShowLeft, {imgShowLeft:size(2) * 0.5, imgShowLeft:size(1) * 0.5}}
imgShowRight = cv.resize {imgShowRight, {imgShowRight:size(2) * 0.5, imgShowRight:size(1) * 0.5}}
file_output_path = path.join(output_path, 'pattern_distribution_left_cam.png')
cv.imwrite {file_output_path, imgShowLeft}
--link_target = path.join('..', self.calibration_folder_name, 'pattern_distribution_left_cam.png')
--current_output_path = path.join(self.current_path, 'pattern_distribution_left_cam.png')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
file_output_path = path.join(output_path, 'pattern_distribution_right_cam.png')
cv.imwrite {file_output_path, imgShowRight}
--link_target = path.join('..', self.calibration_folder_name, 'pattern_distribution_right_cam.png')
--current_output_path = path.join(self.current_path, 'pattern_distribution_right_cam.png')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
cv.imshow {"Pattern distribution (left cam)", imgShowLeft}
cv.waitKey {3000}
cv.imshow {"Pattern distribution (right cam)", imgShowRight}
cv.waitKey {3000}
cv.destroyAllWindows {}
--end
elseif mode == CalibrationMode.SingleCamera then
local imgGetSize = cv.imread {imgDataSingle.imagePaths[1]}
local imgShow = torch.ByteTensor(imgGetSize:size(1), imgGetSize:size(2), 3)
local color = {0, 255, 255}
for i, fn in ipairs(imgDataSingle.imagePaths) do
local fn = imgDataSingle.imagePaths[i]
local img = cv.imread {fn}
img = cv.undistort {src = img, distCoeffs = self.distCoeffs, cameraMatrix = self.cameraMatrix}
local robotPose = imgData.jsposes.recorded_poses[i]
local patternPoseRelToCamera, points3d = self.pattern_localizer:calcCamPose(img, self.cameraMatrix, self.pattern_localizer.pattern, self.configuration.debug_output, nil, self.configuration.circle_pattern_id)
if patternPoseRelToCamera ~= nil then
local cameraPoseRelToPattern = torch.inverse(patternPoseRelToCamera)
local cameraPatternTrafo = cameraPoseRelToPattern -- This is used for an extern camera setup.
if self.configuration.camera_location_mode == 'onboard' then
-- We have an onboard camera setup, thus we have to use patternPoseRelToCamera.
cameraPatternTrafo = patternPoseRelToCamera
end
table.insert(Hc, cameraPatternTrafo)
table.insert(Hg, robotPose)
table.insert(imagesTakenForHandPatternCalib, i)
--if self.configuration.debug_output then
if i < #self.pattern_localizer.colorTab then
color = self.pattern_localizer.colorTab[i]
end
imgShow = printPatternPoints(points3d, self.pattern_localizer.pattern.height, self.pattern_localizer.pattern.width, color, imgShow)
--end
end
end
--if self.configuration.debug_output then
imgShow = cv.resize {imgShow, {imgShow:size(2) * 0.5, imgShow:size(1) * 0.5}}
file_output_path = path.join(output_path, 'pattern_distribution.png')
cv.imwrite {file_output_path, imgShow}
--link_target = path.join('..', self.calibration_folder_name, 'pattern_distribution.png')
--current_output_path = path.join(self.current_path, 'pattern_distribution.png')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
cv.imshow {"Pattern distribution", imgShow}
cv.waitKey {3000}
cv.destroyAllWindows {}
--end
end
-- H = pose of the pattern/camera in TCP coordinate frame
-- 'extern' camera setup: pattern pose in tcp coordinates
-- 'onboard' camera setup: camera pose in tcp coordinates
local H, _, _ = calib.calibrate(Hg, Hc)
if not ransac_outlier_removal then
if self.configuration.camera_location_mode == 'onboard' then
print("Temporary Hand-Eye matrix:") -- TCP <-> Camera
print(H)
else
print("Temporary Hand-Pattern matrix:") -- TCP <-> Pattern
print(H)
end
print("#Hc:")
print(#Hc)
print("#images taken for Hand-Eye/Pattern calib:")
print(#imagesTakenForHandPatternCalib)
print("images taken for Hand-Eye/Pattern calib:")
print(imagesTakenForHandPatternCalib)
else
print("#images that might be taken for Hand-Eye/Pattern calib:")
print(#imagesTakenForHandPatternCalib)
end
local file_output_path = path.join(output_path, 'imagesTakenForHandPatternCalib.t7')
torch.save(file_output_path, imagesTakenForHandPatternCalib)
--local link_target = path.join('..', self.calibration_folder_name, 'imagesTakenForHandPatternCalib.t7')
--local current_output_path = path.join(self.current_path, 'imagesTakenForHandPatternCalib.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
-- RANSAC outlier removal:
-- =======================
if ransac_outlier_removal then
local min_samples = 5
local max_trials = 10
local rot_thres = 1.0 * M_PI / 180.0 -- 1°
local trans_thres = 0.001 -- 1mm
local best_Hc_inliers, best_Hg_inliers, best_hand_eye = ransac_hand_eye(Hc, Hg, min_samples, max_trials, rot_thres, trans_thres)
Hc = {table.unpack(best_Hc_inliers)}
Hg = {table.unpack(best_Hg_inliers)}
H = best_hand_eye:clone()
end
-- perform cross validation
local bestHESolution, alignmentErrorTest, alignmentError = calib.calibrateViaCrossValidation(Hg, Hc, #Hg-2, 5)
bestHESolution = bestHESolution or H
if self.configuration.camera_location_mode == 'onboard' then
print("Best Hand-Eye solution:") -- TCP <-> Camera
print(bestHESolution)
else
print("Best Hand-Pattern solution:") -- TCP <-> Pattern
print(bestHESolution)
end
-- save result and create links at the 'current' folder
if self.configuration.camera_location_mode == 'onboard' then
file_output_path = path.join(output_path, 'HandEye.t7')
torch.save(file_output_path, bestHESolution)
--link_target = path.join('..', self.calibration_folder_name, 'HandEye.t7')
--current_output_path = path.join(self.current_path, 'HandEye.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
self.H_camera_to_tcp = bestHESolution
else
file_output_path = path.join(output_path, 'HandPattern.t7')
torch.save(file_output_path, bestHESolution)
--link_target = path.join('..', self.calibration_folder_name, 'HandPattern.t7')
--current_output_path = path.join(self.current_path, 'HandPattern.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
self.H_pattern_to_tcp = bestHESolution
end
if self.configuration.camera_location_mode == 'onboard' then
file_output_path = path.join(output_path, 'Hc_patternToCam.t7')
torch.save(file_output_path, Hc)
--link_target = path.join('..', self.calibration_folder_name, 'Hc_patternToCam.t7')
--current_output_path = path.join(self.current_path, 'Hc_patternToCam.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
else
file_output_path = path.join(output_path, 'Hc_camToPattern.t7')
torch.save(file_output_path, Hc)
--link_target = path.join('..', self.calibration_folder_name, 'Hc_camToPattern.t7')
--current_output_path = path.join(self.current_path, 'Hc_camToPattern.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
end
file_output_path = path.join(output_path, 'Hg_tcpToBase.t7')
torch.save(file_output_path, Hg)
--link_target = path.join('..', self.calibration_folder_name, 'Hg_tcpToBase.t7')
--current_output_path = path.join(self.current_path, 'Hg_tcpToBase.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
-- calculate camera/pattern pose in base coordinates
if self.configuration.camera_location_mode == 'onboard' then
local patternBaseTrafo = Hg[1] * bestHESolution * Hc[1]
print("base -> pattern trafo (i.e. pattern pose in base coordinates):")
print(patternBaseTrafo)
file_output_path = path.join(output_path, 'PatternBase.t7')
torch.save(file_output_path, patternBaseTrafo)
--link_target = path.join('..', self.calibration_folder_name, 'PatternBase.t7')
--current_output_path = path.join(self.current_path, 'PatternBase.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
return bestHESolution, patternBaseTrafo
else
local cameraBaseTrafo = calc_avg_leftCamBase(bestHESolution, Hg, Hc) --Hg[1] * bestHESolution * Hc[1]
if imgData.jsposes.recorded_pose_of_reference ~= nil then
local cameraRefFrameTrafo = torch.inverse(imgData.jsposes.recorded_pose_of_reference) * cameraBaseTrafo
local rightCamRefFrameTrafo = cameraRefFrameTrafo * torch.inverse(self.rightLeftCamTrafo:double())
print(string.format("%s -> left camera trafo:", self.configuration.camera_reference_frame))
if self.configuration.calibration_mode == "StereoRig" then
print("Note: This is the pose of the left camera with serial: "..self.configuration.cameras[self.configuration.left_camera_id].serial)
print(string.format(" in %s coordinates.", self.configuration.camera_reference_frame))
end
print(cameraRefFrameTrafo)
print(string.format("%s -> right camera trafo:", self.configuration.camera_reference_frame))
if self.configuration.calibration_mode == "StereoRig" then
print("Note: This is the pose of the right camera with serial: "..self.configuration.cameras[self.configuration.right_camera_id].serial)
print(string.format(" in %s coordinates.", self.configuration.camera_reference_frame))
end
print(rightCamRefFrameTrafo)
self.H_cam_to_refFrame = cameraRefFrameTrafo
file_output_path = path.join(output_path, string.format('LeftCam_%s.t7', self.configuration.camera_reference_frame))
file_output_path_right = path.join(output_path, string.format('RightCam_%s.t7', self.configuration.camera_reference_frame))
torch.save(file_output_path, cameraRefFrameTrafo)
torch.save(file_output_path_right, rightCamRefFrameTrafo)
--link_target = path.join('..', self.calibration_folder_name, string.format('LeftCam_%s.t7', self.configuration.camera_reference_frame))
--current_output_path = path.join(self.current_path, string.format('LeftCam_%s.t7', self.configuration.camera_reference_frame))
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
--link_target = path.join('..', self.calibration_folder_name, string.format('RightCam_%s.t7', self.configuration.camera_reference_frame))
--current_output_path = path.join(self.current_path, string.format('RightCam_%s.t7', self.configuration.camera_reference_frame))
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
return bestHESolution, cameraRefFrameTrafo
else
print("base -> camera trafo (i.e. camera pose in base coordinates):")
if self.configuration.calibration_mode == "StereoRig" then
print("Note: For a stereo setup, this is the pose of the left camera with serial: "..self.configuration.cameras[self.configuration.left_camera_id].serial)
end
print(cameraBaseTrafo)
self.H_cam_to_base = cameraBaseTrafo
file_output_path = path.join(output_path, 'LeftCamBase.t7')
torch.save(file_output_path, cameraBaseTrafo)
--link_target = path.join('..', self.calibration_folder_name, 'LeftCamBase.t7')
--current_output_path = path.join(self.current_path, 'LeftCamBase.t7')
--os.execute('rm -f ' .. current_output_path)
--os.execute('ln -s -T ' .. link_target .. ' ' .. current_output_path)
return bestHESolution, cameraBaseTrafo
end
end
end
-- captures a camera image, or in case of 'StereoRig' mode a pair of stereo images
-- detects the camera<->pattern transformation
function HandEye:detectPattern()
--1. capture pair of images
local left_camera_config = self.configuration.cameras[self.configuration.left_camera_id]
local right_camera_config = self.configuration.cameras[self.configuration.right_camera_id]
local left_img
local right_img
local img
local which
if self.configuration.calibration_mode == CalibrationMode.SingleCamera then
if left_camera_config ~= nil and right_camera_config ~= nil then
print("Which camera has been used for hand-eye/pattern calibration?")
print("Please again choose 1 or 2. Then press 'Enter'")
print("1: left camera: "..left_camera_config.serial)
print("2: right camera: "..right_camera_config.serial)
which = io.read("*n")
if which == 1 then
img = self:captureImageNoWait(left_camera_config)
elseif which == 2 then
img = self:captureImageNoWait(right_camera_config)
end
elseif left_camera_config ~= nil then
img = self:captureImageNoWait(left_camera_config)
elseif right_camera_config ~= nil then
img = self:captureImageNoWait(right_camera_config)
end
elseif self.configuration.calibration_mode == CalibrationMode.StereoRig then
left_img = self:captureImageNoWait(left_camera_config)
right_img = self:captureImageNoWait(right_camera_config)
end
--2. detect camera<->pattern trafo
createPatternLocalizer(self)
local ok = false
local cameraPatternTrafo = nil
if self.configuration.calibration_mode == CalibrationMode.StereoRig then
if left_img == nil or right_img == nil then
print("Left and/or right image are \'nil\'.")
return nil
end
ok, cameraPatternTrafo = self.pattern_localizer:calcCamPoseViaPlaneFit(left_img, right_img, 'left', false, nil, self.configuration.circle_pattern_id)
if not ok then
return nil
end
elseif self.configuration.calibration_mode == CalibrationMode.SingleCamera then
if img == nil then
print("Image is \'nil\'.")
return false, nil
end
cameraPatternTrafo, points3d = self.pattern_localizer:calcCamPose(img, self.cameraMatrix, self.pattern_localizer.pattern, false, nil, self.configuration.circle_pattern_id)
if cameraPatternTrafo == nil then
return nil
end
end
return cameraPatternTrafo
end
function HandEye:generateRelativeRotation(O)
local O = O or torch.eye(4,4)
local q = tf.Quaternion.new()
local roll = 0.1 -- 0.0
local pitch = -0.1 -- -0.3
local yaw = 0.0
q:setEuler(yaw, pitch, roll)
local R = torch.eye(4,4)
R[{{1,3}, {1,3}}] = q:toMatrixTensor()
print(R)
return O*R
end
function HandEye:generateRelativeTranslation(O)
local O = O or torch.eye(4,4)
local x_offset = 0.0
local y_offset = 0.0
local z_offset = 0.0
H = torch.eye(4,4)
H[1][4] = x_offset
H[2][4] = y_offset
H[3][4] = z_offset
return O*H
end
-- captures a camera image, or in case of 'StereoRig' mode a pair of stereo images
-- detects the camera<->pattern transformation
-- predicts the camera<->pattern transformation for the case of a robot motion
-- performs the robot motion
function HandEye:movePattern()
-- set the folder to 'current'
--local files_path = self.current_path
local files_path = path.join(self.configuration.output_directory, self.calibration_folder_name)
print('Calling HandEye:movePattern() method')
--if the calibration data is missing, read it from the file
if self.configuration.camera_location_mode == 'onboard' then
if self.H_camera_to_tcp == nil then
self.H_camera_to_tcp = torch.load(files_path .. "/HandEye.t7")
end
else
if self.H_pattern_to_tcp == nil then
self.H_pattern_to_tcp = torch.load(files_path .. "/HandPattern.t7")
end
end
--1. move to the last posture of the taught capture postures
local last_pose = self.configuration.capture_poses[#self.configuration.capture_poses]
print("Moving to the last pose of the taught capture poses ...")
self.move_group:moveJoints(last_pose)
--2. detect the corresponding camera<->pattern trafo
local left_camera_config = self.configuration.cameras[self.configuration.left_camera_id]
local right_camera_config = self.configuration.cameras[self.configuration.right_camera_id]
local left_img
local right_img
local img
local which
if self.configuration.calibration_mode == CalibrationMode.SingleCamera then
if left_camera_config ~= nil and right_camera_config ~= nil then
print("Which camera has been used for hand-eye/pattern calibration?")
print("Please choose 1 or 2. Then press 'Enter'")
print("1: left camera: "..left_camera_config.serial)
print("2: right camera: "..right_camera_config.serial)
which = io.read("*n")
if which == 1 then
img = self:captureImageNoWait(left_camera_config)
elseif which == 2 then
img = self:captureImageNoWait(right_camera_config)
end
elseif left_camera_config ~= nil then
img = self:captureImageNoWait(left_camera_config)
elseif right_camera_config ~= nil then
img = self:captureImageNoWait(right_camera_config)
end
elseif self.configuration.calibration_mode == CalibrationMode.StereoRig then
left_img = self:captureImageNoWait(left_camera_config)
right_img = self:captureImageNoWait(right_camera_config)
end
createPatternLocalizer(self)
local ok = false
local cameraPatternTrafo = nil
if self.configuration.calibration_mode == CalibrationMode.StereoRig then
if left_img == nil or right_img == nil then
print("Left and/or right image are \'nil\'.")
return false, nil
end
ok, cameraPatternTrafo = self.pattern_localizer:calcCamPoseViaPlaneFit(left_img, right_img, 'left', false, nil, self.configuration.circle_pattern_id)
if not ok then
print('pattern not found!')
return ok, cameraPatternTrafo
end
elseif self.configuration.calibration_mode == CalibrationMode.SingleCamera then
if img == nil then
print("Image is \'nil\'.")
return false, nil
end
cameraPatternTrafo, points3d = self.pattern_localizer:calcCamPose(img, self.cameraMatrix, self.pattern_localizer.pattern, false, nil, self.configuration.circle_pattern_id)
if cameraPatternTRafo == nil then
print('pattern not found!')
return false, cameraPatternTrafo
end
end
print('detected cameraPatternTrafo before motion:')
print(cameraPatternTrafo)
--3. compute a relative transformation (corresponding to a motion of the robot)
-- and compute (i.e. predict) the camera<->pattern trafo after this motion
if self.configuration.camera_location_mode == 'onboard' then -- 'onboard' camera setup
if self.H_camera_to_tcp ~= nil then
print('about to do a movement..')
local relative_transformation = self:generateRelativeRotation()
relative_transformation = self:generateRelativeTranslation(relative_transformation)
self.predicted_cameraPatternTrafo = cameraPatternTrafo * relative_transformation
print('prediction for cameraPatternTrafo after motion:')
print(self.predicted_cameraPatternTrafo)
print('compare with the next pattern detection:')
local pose_tcp = self.H_camera_to_tcp * (relative_transformation * torch.inverse(self.H_camera_to_tcp))
local current_pose_tcp = self.move_group:getCurrentPose()
local pose_tcp_in_base_coord = current_pose_tcp:toTensor() * pose_tcp
local transf = tf.Transform.new()
transf:fromTensor(pose_tcp_in_base_coord)
print("transf:")
print(transf)
local datatypes_pose_tcp = datatypes.Pose()
datatypes_pose_tcp:setTranslation(pose_tcp_in_base_coord[{{1,3},4}])
datatypes_pose_tcp:setRotation(transf:getRotation())
local collision_check = false
local end_effector = self.move_group:getEndEffector(self.tcp_end_effector_name)
print("Moving the robot slightly ...")
--end_effector:movePoseLinear(datatypes_pose_tcp, self.configuration.velocity_scaling, collision_check)
end_effector:movePoseLinear(datatypes_pose_tcp, 0.8, collision_check, 0.5)
return ok, self.predicted_cameraPatternTrafo
else
print('please calibrate the robot first')
end
else -- 'extern' camera setup
if self.H_pattern_to_tcp ~= nil then
print('about to do a movement..')
local relative_transformation = self:generateRelativeRotation()
relative_transformation = self:generateRelativeTranslation(relative_transformation)
self.predicted_cameraPatternTrafo = cameraPatternTrafo * relative_transformation
print('prediction for cameraPatternTrafo after motion:')
print(self.predicted_cameraPatternTrafo)
print('compare with the next pattern detection:')
local pose_tcp = self.H_pattern_to_tcp * (relative_transformation * torch.inverse(self.H_pattern_to_tcp))
local current_pose_tcp = self.move_group:getCurrentPose()
local pose_tcp_in_base_coord = current_pose_tcp:toTensor() * pose_tcp
local transf = tf.Transform.new()
transf:fromTensor(pose_tcp_in_base_coord)
local datatypes_pose_tcp = datatypes.Pose()
datatypes_pose_tcp:setTranslation(pose_tcp_in_base_coord[{{1,3},4}])
datatypes_pose_tcp:setRotation(transf:getRotation())
local collision_check = false
local end_effector = self.move_group:getEndEffector(self.tcp_end_effector_name)
print("Moving the robot slightly ...")
--end_effector:movePoseLinear(datatypes_pose_tcp, self.configuration.velocity_scaling, collision_check)
end_effector:movePoseLinear(datatypes_pose_tcp, 0.8, collision_check, 0.5)
return ok, self.predicted_cameraPatternTrafo
else
print('please calibrate the robot first')
end
end
end
-- Compute some metric for the evaluation of the hand-eye calibration
local function metricCalculation(prediction, detection)
local H1 = tf.Transform.new()
local H2 = tf.Transform.new()
H1:fromTensor(prediction)
H2:fromTensor(detection)
local R1 = prediction[{{1,3}, {1,3}}]
local R2 = detection[{{1,3}, {1,3}}]
-- translation error (norm of difference)
local err_t = torch.norm(prediction[{{1,4}, {4}}] - detection[{{1,4}, {4}}])
-- translation error for each axis
local err_axes = prediction[{{1,4}, {4}}] - detection[{{1,4}, {4}}]
-- rotation error (norm of difference of Euler angles)
local err_r = torch.norm(H1:getRotation():toTensor() - H2:getRotation():toTensor())
-- error given by the angle from the axis–angle representation of rotation
-- the angle of rotation of a matrix R in the axis–angle representation is given by arccos( {Tr(R) -1} /2)
-- if R1 ~= R2 => R1 * R2.inv() ~= Identity => angle of rotation ~= 0
-- => arccos(angle of rotation) ~= pi/2 = 1.57..
local err_r2 = torch.acos( torch.trace(R1* torch.inverse(R2) -1) / 2 )
local err_r3 = torch.abs(err_r2 - M_PI/2)
print('translation error (norm of difference) [in m]:', err_t, ' ( = ', err_t * 1000, ' mm)')
print('translation error for each axis [in mm]:')
print(err_axes * 1000)
print('euler angles prediction:')
print(H1:getRotation():toTensor())
print('euler angles detection:')
print(H2:getRotation():toTensor())
print('rotation error (norm of difference of Euler angles) [in radians]:', err_r, ' ( = ', err_r * 180/M_PI, ' degree)')
print('rotation error metric #2 [in radians]:', err_r3, ' ( = ', err_r3 * 180/M_PI, ' degree)')
return err_t, err_axes, err_r, err_r3
end
-- Evaluation of the hand eye calibration (with only one robot movement)
function HandEye:evaluateCalibration()
print('HandEye:evaluateCalibration()')
local ok, prediction = self:movePattern() -- stores the predicted pose of the pattern at self.predicted_cameraPatternTrafo and returns it
if not ok then
print('aborting evaluation')
return
end
local detection = self:detectPattern()
if detection == nil then
print('pattern not detected, aborting evaluation')
return