Alice: Road Following: Difference between revisions
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== Lecture Materials == | == Lecture Materials == | ||
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[[Media:L4-3_Alice_RoadEst.pdf|Lecture: Alice Road Estimation]] | |||
== Reading == | == Reading == | ||
Latest revision as of 00:54, 22 April 2006
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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.
