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Course description
This course will cover mobile robot localization and mapping in depth.
We will develop a thorough understanding of the current
state-of-the-art, including Kalman filter and Markov/Monte Carlo
approaches to localization. We will be reading papers from the
research literature, and there will be a final project for the course.
We will spend some time looking at related work in computer vision, as
well as approaches to multiple robot mapping and localization.
The only formal prerequisite is DSA, but familiarity with the basic
ideas/mathematics of localization (least squares estimation and
probability and statistics) as well as a reasonably sophisticated math
background (linear algebra and probability) is expected. This is a
graduate-level class.
General information
| Time:
| Mondays and Thursdays, 10:00 – 11:20am
|
| Location:
| TBA
|
| Prerequisite:
| CSCI 2300 Data Structures and Algorithms (also, see description)
|
Handouts