Alice: Road Following: Difference between revisions
From Murray Wiki
Jump to navigationJump to search
No edit summary |
|||
| (3 intermediate revisions by the same user not shown) | |||
| Line 2: | Line 2: | ||
<!-- Enter a 1 paragraph description of the contents of the lecture. Make sure to include any key concepts, so that the wiki search feature will pick them up --> | <!-- Enter a 1 paragraph description of the contents of the lecture. Make sure to include any key concepts, so that the wiki search feature will pick them up --> | ||
This lecture will present a practical example application for the estimation tools presented in the Monday and Wednesday lectures. The example application is road estimation and tracking for off-highway navigation. I'll present implementation details, resources and results from live testing of an extended Kalman filter implementation, and provide resources and preliminary results for implementing moving horizon estimation to solve the same problem. | This lecture will present a practical example application for the estimation tools presented in the Monday and Wednesday lectures. The example application is road estimation and tracking for off-highway navigation. | ||
I'll present implementation details, resources and results from live testing of an extended Kalman filter implementation, and provide resources and preliminary results for implementing moving horizon estimation to solve the same problem. --[[User:Lars|Lars]] 09:08, 21 April 2006 (PDT) | |||
== Lecture Materials == | == Lecture Materials == | ||
<!-- Include links to materials that you used in your lecture. At a minimum, this should include a link to your lecture presentation. You might also include links to MATLAB scripts or other source code that students would find useful --> | <!-- Include links to materials that you used in your lecture. At a minimum, this should include a link to your lecture presentation. You might also include links to MATLAB scripts or other source code that students would find useful --> | ||
<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] --> | <!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] --> | ||
[[Media:L4-3_Alice_RoadEst.pdf|Lecture: Alice Road Estimation]] | |||
== Reading == | == Reading == | ||
| Line 19: | Line 21: | ||
== Additional Resources == | == Additional Resources == | ||
<!-- Links to additional information. If there are good sources of additional information for students interested in exploring this topic further, these should go at the bottom of the page. --> | <!-- Links to additional information. If there are good sources of additional information for students interested in exploring this topic further, these should go at the bottom of the page. --> | ||
* The Open Source Computer Vision | * The Open Source Computer Vision (OpenCV) Library has a well-documented C++ class that implements the Kalman filter, and a host of image processing tools. See http://www.intel.com/technology/computing/opencv/ and http://opencvlibrary.sourceforge.net/ | ||
* An Octave (MATLAB clone) toolbox for implementing nonlinear receding horizon control and moving horizon estimation is available at http://jbrwww.che.wisc.edu/home/tenny/nmpc/. The release is somewhat old (2003); be sure to get the CVS version to avoid some known bugs. | * An Octave (MATLAB clone) toolbox for implementing nonlinear receding horizon control and moving horizon estimation is available at http://jbrwww.che.wisc.edu/home/tenny/nmpc/. The release is somewhat old (2003); be sure to get the CVS version to avoid some known bugs. | ||
* For numerical solving, SNOPT is commercial software, but CDS cluster has a license for it. NPSOL is another option. SNOPT 5.3 user manual available at http://citeseer.ist.psu.edu/gill99users.html. | |||
Latest revision as of 00:54, 22 April 2006
| Prev: MHE | Course Home | Next: Packet-based Estimation |
This lecture will present a practical example application for the estimation tools presented in the Monday and Wednesday lectures. The example application is road estimation and tracking for off-highway navigation.
I'll present implementation details, resources and results from live testing of an extended Kalman filter implementation, and provide resources and preliminary results for implementing moving horizon estimation to solve the same problem. --Lars 09:08, 21 April 2006 (PDT)
Lecture Materials
Lecture: Alice Road Estimation
Reading
- Cf. (extended) Kalman filter reading materials from Monday 4/17/06 lecture.
- Cf. Moving Horizon Estimation reading materials from Wednesday 4/19/06 lecture.
- Model-Based Estimation of Off-Highway Road Geometry using Single-Axis LADAR and Inertial Sensing, Lars B. Cremean and Richard M. Murray, To appear, Proc. of 2006 International Conference on Robotics and Automation
Additional Resources
- The Open Source Computer Vision (OpenCV) Library has a well-documented C++ class that implements the Kalman filter, and a host of image processing tools. See http://www.intel.com/technology/computing/opencv/ and http://opencvlibrary.sourceforge.net/
- An Octave (MATLAB clone) toolbox for implementing nonlinear receding horizon control and moving horizon estimation is available at http://jbrwww.che.wisc.edu/home/tenny/nmpc/. The release is somewhat old (2003); be sure to get the CVS version to avoid some known bugs.
- For numerical solving, SNOPT is commercial software, but CDS cluster has a license for it. NPSOL is another option. SNOPT 5.3 user manual available at http://citeseer.ist.psu.edu/gill99users.html.
