An Extended Kalman Filter 2010 - Revision history

Robot Lab 2010: /* Code: */

2010-03-24T00:29:47Z

Code:

2010-03-24T00:29:30Z

2010-03-21T08:08:56Z

How the project works:

2010-03-21T08:07:12Z

How the project works:

2010-03-21T08:00:49Z

Conclusion:

2010-03-21T07:59:15Z

Useful Links:

2010-03-21T07:56:41Z

How the project works:

2010-03-21T07:52:25Z

How the project works:

2010-03-21T07:51:55Z

2010-03-21T07:48:23Z

Useful Links:

← Older revision		Revision as of 00:29, 24 March 2010
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	Code can be downloaded from [https://docs.google.com/leaf?id=0ByOY6vHGMB8PZjk1ZDJlODQtMWZiYS00NDI1LWEwNmMtMDUwNmJmZmY3YzJm&hl=en here]		Code can be downloaded from [https://docs.google.com/leaf?id=0ByOY6vHGMB8PZjk1ZDJlODQtMWZiYS00NDI1LWEwNmMtMDUwNmJmZmY3YzJm&hl=en here]
		+
	To run the code, run SlamSimulation.java.		To run the code, run SlamSimulation.java.

@@ Line 159: / Line 159: @@
 Code can be downloaded from [https://docs.google.com/leaf?id=0ByOY6vHGMB8PZjk1ZDJlODQtMWZiYS00NDI1LWEwNmMtMDUwNmJmZmY3YzJm&hl=en here]
+To run the code, run SlamSimulation.java.
 == Useful Links: ==

← Older revision		Revision as of 08:08, 21 March 2010
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	3. '''Update state:'''		3. '''Update state:'''
		+
	For each re-observed landmarks, update estimated state using re-observed landmarks using the following steps:		For each re-observed landmarks, update estimated state using re-observed landmarks using the following steps:

@@ Line 44: / Line 44: @@
               DeltaX = change of x = velocity*deltaT * cos(theta)
               B<sub>k</sub> = Input gain matrix
-                            = [ Cos(theta)	0]
+                            = [Cos(theta) 0]
-                              [Sin(theta)	0]
+                              [Sin(theta) 0]
-                              [0	                1]
+                              [0	         1]
               u<sub>k</sub> = control input vector
                             = [deltaT*velocity, deltaTheta]<sup>T</sup>

← Older revision		Revision as of 08:00, 21 March 2010
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	3. Data association is almost perfect.		3. Data association is almost perfect.
		+
		+	In most real world situation, these assumption do not hold. More robust methods are needed in those cases.

	== Code: ==		== Code: ==

← Older revision		Revision as of 07:59, 21 March 2010
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	3. [http://www.ri.cmu.edu/pub_files/pub4/kurth_derek_2004_1/kurth_derek_2004_1.pdf Range-Only Robot Localization and SLAM with Radio]		3. [http://www.ri.cmu.edu/pub_files/pub4/kurth_derek_2004_1/kurth_derek_2004_1.pdf Range-Only Robot Localization and SLAM with Radio]
		+
		+	4.[http://ai.stanford.edu/~paskin/slam/ JavaSlam]

@@ Line 59: / Line 59: @@
                   and W = white noise = [DeltaX DeltaY DeltaTheta]<sup>T</sup>
-. For each re-observed landmarks, update estimated state using re-observed landmarks using the following steps:
+. '''Update state:'''
 a) Compute innovation covariances:

@@ Line 17: / Line 17: @@
 Detailed process is explained below:
-.	Observe and associate landmarks:
+.	'''Observe and associate landmarks''':
 For simplicity, I assumed true data association. That means whenever the robot sees a landmark it knows which landmark is this. Each landmark has id.  Landmarks are stored in an array. If landmark with id n is not in the landmark array, it is added to the array.
-.	Predict next state based on odometry:
+.	'''Predict next state using on odometry''':
 The robots state is defined as X = [ x y theta]
@@ Line 94: / Line 94: @@
 '''
 ==Java library for SLAM ==
 '''

@@ Line 150: / Line 150: @@
 . Data association is almost perfect.
 == Code: ==
@@ Line 165: / Line 163: @@
 . [http://www.ri.cmu.edu/pub_files/pub4/kurth_derek_2004_1/kurth_derek_2004_1.pdf Range-Only Robot Localization and SLAM with Radio]

← Older revision		Revision as of 07:48, 21 March 2010
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	3. [http://www.ri.cmu.edu/pub_files/pub4/kurth_derek_2004_1/kurth_derek_2004_1.pdf Range-Only Robot Localization and SLAM with Radio]		3. [http://www.ri.cmu.edu/pub_files/pub4/kurth_derek_2004_1/kurth_derek_2004_1.pdf Range-Only Robot Localization and SLAM with Radio]
		+
		+	4. [http://ai.stanford.edu/~paskin/slam/ JavaSlam ]