Difference between revisions of "Indoor localization system based on a gyro and visual passive markers"

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(The Project in short)
(The Project in short)
Line 17: Line 17:
  
 
===The Project in short===
 
===The Project in short===
#. Predict new robot pose using         
+
#. Predict new robot pose
#. Modify the covariance following (2.5).       
+
#. Modify the covariance
 
#. For each of the M observed landmarks i:         
 
#. For each of the M observed landmarks i:         
##. Compute hi (2.12) and its Jacobian.         
+
##. Compute hi and its Jacobian.         
 
##. For each dimension j of the observation hi:         
 
##. For each dimension j of the observation hi:         
### Compute the scalar (2.19).       
+
### Compute the scalar Sij
### Computation of  (4.1).         
+
### Computation of  Kij.         
### Update the filter state vector using (4.2).       
+
### Update the filter state vector  
### Update the filter covariance Pk | k using (4.3).       
+
### Update the filter covariance Pk | k
 
#. If necessary, introduce new landmarks in the map.
 
#. If necessary, introduce new landmarks in the map.

Revision as of 09:56, 4 June 2010

Indoor localization system based on a gyro and visual passive markers
Short Description: This project aims at developing autonomous moving systems based on a gyro and passive markers
Coordinator:
Tutor: MatteoMatteucci (matteo.matteucci@polimi.it), SimoneCeriani (ceriani@elet.polimi.it), DavideMigliore (d.migliore@evidence.eu.com)
Collaborator:
Students: DarioCecchetto (dario.cecchetto@mail.polimi.it), LorenzoConsolaro ()
Research Topic:
Start: 2009/06/01
End: 2010/04/25
Status: Active
Level: Bs
Type: Thesis

What we use

  • Eclipse for C/C++
  • An STR9 Cam based module from ST
  • A STM32 Gyroscope based module from ST

The Project in short

  1. . Predict new robot pose
  2. . Modify the covariance
  3. . For each of the M observed landmarks i:
    1. . Compute hi and its Jacobian.
    2. . For each dimension j of the observation hi:
      1. Compute the scalar Sij
      2. Computation of Kij.
      3. Update the filter state vector
      4. Update the filter covariance Pk | k
  4. . If necessary, introduce new landmarks in the map.