CMSC389M
Special Topics in Computer Science; SLAM: Why Robots Don't Crash
Students will be provided with a practical and lightly theoretical understanding of the most popular algorithms that solve the Simultaneous Localization and Mapping (SLAM) problem to enable self driving car technology. An emphasis will be placed on the probabilistic methods that underpin the SLAM problem.
Sister Courses: CMSC389A, CMSC389B, CMSC389C, CMSC389E, CMSC389F, CMSC389G, CMSC389I, CMSC389J, CMSC389K, CMSC389L, CMSC389N, CMSC389O, CMSC389P, CMSC389Q, CMSC389R, CMSC389T, CMSC389U, CMSC389V, CMSC389W, CMSC389X, CMSC389Y, CMSC389Z
Past Semesters
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During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.
Average GPA of 2.52 between 9 students*
* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.