def pc2CB(self, msg):
if self.init:
print "---------------------"
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
os1_trans = self.getTransform("map", "os1_sensor")
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(cloud, 'x', self.x_min, self.x_max)
fil_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'y', self.y_min, self.y_max)
fil_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'intensity', 330, 4096)
# Turn XYZI cloud into XYZ cloud
XYZ_cloud = pcl_helper.XYZI_to_XYZ(fil_cloud)
# Statistical outlier filter
fil = XYZ_cloud.make_statistical_outlier_filter()
fil.set_mean_k(10)
fil.set_std_dev_mul_thresh(0.1)
filtered_cloud = fil.filter()
pc2_msg = pcl_helper.pcl_to_ros(filtered_cloud, "base_link")
self.pc2_pub.publish(pc2_msg)
# Get groups of cluster of points
clusters = self.getClusters(filtered_cloud, 0.05, 5, 350)
print "len(clusters)", len(clusters)
for i in range(0,len(clusters)):
print "len(clusters[", i, "]", len(clusters[i])
# Get mean points from clusters
mean_pts = []
for cluster in clusters:
mean_pts.append(self.getMeanPoint(cluster, filtered_cloud))
print "len(mean_pts)", len(mean_pts)
# for i in range(0,len(mean_pts)):
# print "mean_pts[", i, "]", mean_pts[i]
# Remove other points, leave the table points only
table_pts = []
table_pts = self.getTablePoints(mean_pts)
print "len(table_pts)", len(table_pts)
# print table_pts
for i in range(len(table_pts)):
self.vizPoint(i, table_pts[i], ColorRGBA(1,1,1,1), "base_link")
# Finding 2 middle points from 4 table legs and use them as path_pts
path_pts = []
if(len(table_pts)==4):
for p1 in table_pts:
for p2 in table_pts:
if(p1==p2):
break
# Register 2 middle_pts of 4 table legs
if(0.73 < self.getDistance(p1, p2) < 0.83):
path_pts.append(self.getMiddlePoint(p1, p2))
break
# print "len(path_pts)", len(path_pts)
# print path_pts
# Turn path_pts into map frame
for i in range(len(path_pts)):
path_pts[i] = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(path_pts[i], Quaternion(0,0,0,1)), trans).pose.position
path_pts[i] = Point(path_pts[i].x, path_pts[i].y, 0)
# Record the starting point and heading once
if self.record_once:
# self.start_pt = trans.transform.translation
self.start_pt = os1_trans.transform.translation
self.record_once = False
# Create a list with distance information between initial_pt and path_pts
distance_list = [self.getDistance(p, self.start_pt) for p in path_pts]
# Rearrange the path_pts to be in descending order
path_pts = self.getDescend(path_pts, distance_list)
# Duplicate the path_pts to prevent from being edited
table_pathpts = path_pts[:]
# print "table_pathpts: ", len(table_pathpts)
if(len(table_pathpts)==2):
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(table_pathpts)
# Insert another point on the line with d[m] away from the 2nd table_pathpts
path_pts.append(self.getPointOnLine(m1, 1.0, table_pathpts[1], self.start_pt))
# Visualize the path_pts
self.vizPoint(10, path_pts[0], ColorRGBA(1,0,0,1), "map") # Red
self.vizPoint(11, path_pts[1], ColorRGBA(1,1,0,1), "map") # Yellow
self.vizPoint(12, path_pts[2], ColorRGBA(1,0,1,1), "map") # Magenta
# print len(path_pts)
# Find the heading of the table (last 2 path_pts)
heading_q = self.getOrientation(table_pathpts[0], table_pathpts[1])
# Publish the path
self.route_pub.publish(self.getPath2Table(path_pts, heading_q))
def pc2CB(self, msg):
if self.init:
print "start_time", rospy.Time.now().to_sec()
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(cloud, 'x', self.x_min, self.x_max)
filtered_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'y', self.y_min, self.y_max)
# Turn cloud into colorless cloud
# colorless_cloud = pcl_helper.XYZRGB_to_XYZ(filtered_cloud)
colorless_cloud = pcl_helper.XYZI_to_XYZ(filtered_cloud)
# Get groups of cluster of points
# clusters = self.getClusters(colorless_cloud, 0.05, 20, 250) # Table on Top AGV - 60~230
clusters = self.getClusters(colorless_cloud, 0.05, 20, 350)
# print "len(clusters)", len(clusters)
# for i in range(0,len(clusters)):
# print "len(clusters[", i, "]", len(clusters[i])
# Get mean points from clusters
mean_pts = []
for cluster in clusters:
mean_pts.append(self.getMeanPoint(cluster, filtered_cloud))
# print "len(mean_pts)", len(mean_pts)
# for i in range(0,len(mean_pts)):
# print "mean_pts[", i, "]", mean_pts[i]
# Remove other points, leave the table points only
table_pts = []
table_pts = self.getTablePoints(mean_pts)
# print "len(table_pts)", len(table_pts)
# print table_pts
for i in range(len(table_pts)):
self.vizPoint(i, table_pts[i], ColorRGBA(1,1,1,1), "base_link")
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Finding 2 middle points from 4 table legs and use them as path_pts
path_pts = []
if(len(table_pts)==4):
for p1 in table_pts:
for p2 in table_pts:
if(p1==p2):
break
# Register 2 middle_pts of 4 table legs
if(0.73 < self.getDistance(p1, p2) < 0.83):
path_pts.append(self.getMiddlePoint(p1, p2))
break
# print "len(path_pts)", len(path_pts)
# print path_pts
# Path_pts that generated from between 2 legs of table must be fulfill below requirement, else return
if(len(path_pts)==2):
if not (0.33 < self.getDistance(path_pts[0], path_pts[1]) < 0.48):
return
# Turn path_pts into map frame
for i in range(len(path_pts)):
path_pts[i] = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(path_pts[i], Quaternion(0,0,0,1)), trans).pose.position
# Record the starting point once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Create a list with distance information between initial_pt and path_pts
distance_list = [self.getDistance(p, self.start_pt) for p in path_pts]
# Rearrange the path_pts to be in ascending order
path_pts = self.getAscend(path_pts, distance_list)
# Duplicate the path_pts to prevent from being edited
table_pathpts = path_pts[:]
if(len(table_pathpts)==2):
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(table_pathpts)
# Insert another point on the line with d[m] away from the 1st table_pathpts
path_pts.insert(0, self.getPointOnLine(m1, 1.0, table_pathpts[0], self.start_pt))
# Insert a point ahead d[m] of table to the begin of path_pts
# ahead_pt = self.getPointAheadTable(table_pathpts, 1.0)
# path_pts.insert(0, ahead_pt)
# Insert a point ahead d[m] of table to the middle of table_pathpts
# midway_pt = self.getPointAheadTable(table_pathpts, -0.4)
# path_pts.insert(2, midway_pt)
# Insert another point on the line with d[m] away from the 1st table_pathpts
# path_pts.insert(2, self.getPointOnLine(m1, 0.05, table_pathpts[1], self.start_pt))
# Remove the last point of path_pts
# path_pts = path_pts[:len(path_pts)-1]
# Add the AGV start_pt to the begin of path_pts
# path_pts.insert(0, self.start_pt)
# Visualize the path_pts
# self.vizPoint(0, path_pts[0], ColorRGBA(1,0,0,1), "map") # Red
# self.vizPoint(1, path_pts[1], ColorRGBA(1,1,0,1), "map") # Yellow
# self.vizPoint(2, path_pts[2], ColorRGBA(0,0,1,1), "map") # Blue
# Find the heading of the table (last 2 path_pts)
heading_q = self.getOrientation(table_pathpts[0], table_pathpts[1])
# Publish the path
self.route_pub.publish(self.getPath2Table(path_pts, heading_q))
# print "-----------------"
print "end_time", rospy.Time.now().to_sec()
def pc2CB(self, msg):
if self.init:
self.start_time = rospy.Time.now().to_sec()
print "-----------------"
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', self.x_min, self.x_max)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', self.y_min, self.y_max)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'intensity', 450, 4096)
# Statistical outlier filter
fil = pcl_helper.XYZI_to_XYZ(
fil_cloud).make_statistical_outlier_filter()
fil.set_mean_k(10)
fil.set_std_dev_mul_thresh(0.05) #1.0 / 0.1
filtered_cloud = fil.filter()
pc2_msg = pcl_helper.pcl_to_ros(filtered_cloud, "base_link")
self.pc2_pub.publish(pc2_msg)
# Get groups of cluster of points
clusters = self.getClusters(filtered_cloud, 0.05, 10, 800)
print "len(clusters)", len(clusters)
for i in range(len(clusters)):
print "clusters[" + str(i) + "]", len(clusters[i])
# Not enough clusters to be identified as charger unit
if (len(clusters) < 3):
return
# self.middle_time_1 = rospy.Time.now().to_sec()
# Extract charger_wall & charger_pillars clusters
# - use for compute gradient of Line of Best Fit
# - use for compute middle_pt
wall_cluster, pillar_clusters = self.getChargerClusters(
clusters, filtered_cloud)
# self.middle_time_2 = rospy.Time.now().to_sec()
# Cannot find the charger_wall and charger_pillars
if (wall_cluster == None or pillar_clusters == []):
print "wall cluster or pillar clusters not found"
return
print "wall_cluster", len(wall_cluster)
self.vizPoint(
0, self.getMeanPointFromCluster(wall_cluster, filtered_cloud),
ColorRGBA(0, 1, 0, 1), "base_link")
for i in range(len(pillar_clusters)):
print "pillar_clusters[" + str(i) + "]", len(
pillar_clusters[i])
self.vizPoint(
i + 1,
self.getMeanPointFromCluster(pillar_clusters[i],
filtered_cloud),
ColorRGBA(0, 1, 0, 1), "base_link")
# Get Points of wall_cluster
wall_pts = self.getPointsFromCluster(wall_cluster, filtered_cloud)
# Turn wall_pts into map frame
for i in range(len(wall_cluster)):
wall_pts[i] = tf2_geometry_msgs.do_transform_pose(
self.toPoseStamped(wall_pts[i], Quaternion(0, 0, 0, 1)),
trans).pose.position
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(wall_pts)
print "gradient", m1
pillar_pts = []
# Get mean points from pillar_clusters
for cluster in pillar_clusters:
pillar_pts.append(
self.getMeanPointFromCluster(cluster, filtered_cloud))
# Finding 1 middle_pt from 2 charger_pillars and use it as path_pts
print pillar_pts
if (len(pillar_pts) == 2):
middle_pt = None
for p1 in pillar_pts:
for p2 in pillar_pts:
if (p1 == p2):
break
# Only register middle_pt of 2 charger_pillars points
if (0.5 < self.getDistance(p1, p2) < 0.65):
middle_pt = self.getMiddlePoint(p1, p2)
break
if (middle_pt == None):
"middle_pt not found"
return
# Turn middle_pt into map frame
middle_pt = tf2_geometry_msgs.do_transform_pose(
self.toPoseStamped(middle_pt, Quaternion(0, 0, 0, 1)),
trans).pose.position
# Register the middle_pt as one of the path_pts
path_pts = []
path_pts.append(middle_pt)
# Record the starting point and heading once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(
0,
self.getPointOnNormalLine(m1, 1.5, middle_pt,
self.start_pt))
# Duplicate the path points to prevent from being edited
charger_pathpts = path_pts[:]
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(0,
self.getPointOnNormalLine(
m1, 0.29, middle_pt, self.start_pt)) #0.27
# Create a list with distance information between init_pt and path_pts
distance_list = [
self.getDistance(p, self.start_pt) for p in path_pts
]
# Rearrange the path_pts to be in ascending order
path_pts = self.getAscend(path_pts, distance_list)
# Add the AGV start_pt to the begin of path_pts
# path_pts.insert(0, self.start_pt)
# Remove last point of path_pts (middle pt of 2 charger feature pts)
path_pts = path_pts[:len(path_pts) - 1]
print "len(path_pts)", len(path_pts)
# # Visualize the path_pts
self.vizPoint(0, path_pts[0], ColorRGBA(1, 0, 0, 1),
"map") # Red
self.vizPoint(1, path_pts[1], ColorRGBA(1, 1, 0, 1),
"map") # Yellow
# self.vizPoint(2, path_pts[2], ColorRGBA(0,0,1,1), "map") # Blue
# Find the heading of the charger (last 2 path_pts)
heading_q = self.getOrientation(charger_pathpts[0],
charger_pathpts[1])
# Publish the path
self.route_pub.publish(
self.getPath2Charger(path_pts, heading_q))
print "start_time", self.start_time
# print "middle_time_1", self.middle_time_1
# print "middle_time_2", self.middle_time_2
print "end_time", rospy.Time.now().to_sec()