X-Git-Url: https://defiant.homedns.org/gitweb/?p=ros_wild_thumper.git;a=blobdiff_plain;f=scripts%2Fdwm1000.py;h=b16ff9ecb30faca5ef2727d391bb15e7866da337;hp=707dff19f05e75865b6371b789daf84a1159db86;hb=2f194b3fe82009a77b79021f0c57e6ca55c1706f;hpb=f04118a96832b33d160805b1e07e738a41102b84

diff --git a/scripts/dwm1000.py b/scripts/dwm1000.py
index 707dff1..b16ff9e 100755
--- a/scripts/dwm1000.py
+++ b/scripts/dwm1000.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: iso-8859-15 -*-
 
-VISULAIZE = False
+VISUALIZE = False
 
 import threading
 import struct
@@ -15,36 +15,10 @@ from time import sleep
 from std_msgs.msg import Float32
 from nav_msgs.msg import Odometry
 from wild_thumper.srv import DWM1000Center, DWM1000CenterResponse
-if VISULAIZE:
+from pyshared.simple_kalman import simple_kalman
+if VISUALIZE:
 	import matplotlib.pyplot as plt
 
-class simple_kalman:
-	def __init__(self, x_est, P_est, Q, R):
-		self.x_est = x_est # Systemzustand
-		self.P_est = P_est # Fehlerkovarianz
-		self.Q = Q # Systemrauschen
-		self.R = R # Varianz des Messfehlers
-
-	def run(self, y):
-		# Korrektur mit der Messung
-		# (1) Berechnung der Kalman Verstärkung
-		K = self.P_est/(self.R + self.P_est)
-		# (2) Korrektur der Schätzung mit der Messung y
-		x = self.x_est + K*(y - self.x_est)
-		# (3) Korrektur der Fehlerkovarianzmatrix
-		P = (1-K)*self.P_est
-		#
-		# Prädiktion
-		# (1) Prädiziere den Systemzustand
-		self.x_est = x
-		# (2) Präzidiere die Fehlerkovarianzmatrix
-		self.P_est = P + self.Q
-
-		return x
-
-	def set_measure_cov(self, R):
-		if R > 0:
-			self.R = R
 
 class DW1000(threading.Thread):
 	def __init__(self, name, addr, offset):
@@ -90,19 +64,10 @@ class DW1000(threading.Thread):
 
 class Position:
 	def __init__(self):
-		# Varianz des Messfehlers
-		Rx = 0.2
-		Ry = 0.05
-		# Fehlerkovarianz
-		P_est_x = 0.02
-		P_est_y = 0.01
-		# Systemrauschen
-		Qx = 0.001
-		Qy = 0.00001
-		# TODO: Replace 2x 1d kalman with 2d kalman
-		# so the polyfit variance can be replaced with a covariance matrix
-		self.filter_x = simple_kalman(1.0, P_est_x, Qx, Rx)
-		self.filter_y = simple_kalman(0.0, P_est_y, Qy, Ry)
+		R = np.matrix([[0.5, 0.0], [0.0, 0.2]])		# Varianz des Messfehlers
+		P_est = 1e50 * np.eye(2)			# Fehlerkovarianz
+		Q = np.matrix([[0.001, 0.0], [0.0, 0.0001]])	# Systemrauschen
+		self.filter_xy = simple_kalman(np.array([[1.0, 0.0]]).T, P_est, Q, R)
 		self.speed_x = 0
 		self.speed_y = 0
 		self.speed_z = 0
@@ -119,7 +84,7 @@ class Position:
 		current_time = rospy.Time.now()
 		dt = (current_time - self.last_time).to_sec()
 		# Subtract vehicle speed
-		pos = np.array([self.filter_x.x_est, self.filter_y.x_est])
+		pos = self.filter_xy.x_est.T
 		# translation
 		pos -= np.array([self.speed_x*dt, self.speed_y*dt])
 		# rotation
@@ -127,21 +92,25 @@ class Position:
 				[np.sin(self.speed_z*dt),  np.cos(self.speed_z*dt)]])
 		pos = np.dot(pos, rot)
 		# copy back
-		self.filter_x.x_est = pos[0]
-		self.filter_y.x_est = pos[1]
+		self.filter_xy.x_est = pos.T
 
 		# run kalman if new measurements are valid
+		pos = None
 		if x != None and y != None:
-			x = self.filter_x.run(x)
-			y = self.filter_y.run(y)
+			pos = self.filter_xy.run(np.array([[x, y]]).T)
 
 			# Update variance
 			dist = np.linalg.norm([x, y])
-			self.filter_x.set_measure_cov(np.polyval([0.018, 0.0, 0.0], dist))
-			self.filter_y.set_measure_cov(np.polyval([0.006, 0.0, 0.0], dist))
+			cov_00 = np.polyval([0.018, 0.0, 0.0], dist)
+			cov_11 = np.polyval([0.006, 0.0, 0.0], dist)
+			self.filter_xy.set_measure_cov(np.matrix([[cov_00, 0.0], [0.0, cov_11]]))
 		else:
-			x = self.filter_x.x_est
-			y = self.filter_y.x_est
+			pos = self.filter_xy.x_est
+			self.filter_xy.P_est = np.matrix(1e50 * np.eye(2)) # reset estimate when lost
+
+
+		x = pos.item((0, 0))
+		y = pos.item((1, 0))
 
 		self.last_time = current_time
 		return x,y
@@ -156,8 +125,8 @@ def handle_center_call(req):
 
 if __name__ == "__main__":
 	rospy.init_node('DWM1000', log_level=rospy.DEBUG)
-	dwleft  = DW1000("uwb_dist_left",  0xc2, +0.0)
-	dwright = DW1000("uwb_dist_right", 0xc0, -0.0)
+	dwleft  = DW1000("uwb_dist_left",  0xc2, -0.04)
+	dwright = DW1000("uwb_dist_right", 0xc0, +0.04)
 	dist_l_r = 0.285 # Distance between both DWM1000
 	rate = rospy.Rate(10)
 	pos = Position()
@@ -178,7 +147,7 @@ if __name__ == "__main__":
 
 			diff = abs(dist_left - dist_right)
 			if diff >= dist_l_r:
-				# difference to high, correct to maximum
+				# difference too high, correct to maximum
 				off = diff - dist_l_r + 0.01
 				if dist_left > dist_right:
 					dist_left -= off/2
@@ -199,7 +168,7 @@ if __name__ == "__main__":
 
 				x, y = pos.filter(x, y)
 
-				if VISULAIZE:
+				if VISUALIZE:
 					circle_left = plt.Circle((-dist_l_r/2, 0), dwleft.distance, color='red', fill=False)
 					circle_right = plt.Circle((dist_l_r/2, 0), dwright.distance, color='green', fill=False)
 					plt.gca().add_patch(circle_left)