Welcome!

The 2026 offering of 6213 will explore the field of State Estimation, and do so through applications in autonomous vehicle localization and navigation. Topics will include a review of probability, state or belief representations, and an introduction to several popular filters including Bayes Filters, Kalman Filters, Extended Kalman Filters, Unscented Kalman Filters, and Particle Filters. The course will include a series of labs where students apply the different filters to real data. The course will culminate in a self-designed project in which students must collect their own data.

course Instructor

• Chris Clark – c.clark AT nyu.edu

  • Office Hours: 12:30 - 2:00pm Mondays

  • Location: MetroTech 05, Room 130A

Course Assistant

• Dhruv Gadkari - dvg6268 AT nyu.edu 

  • Office Hours: 12:00 to 2:00pm Thursdays

Recommended Texts

No textbook is required, but we recommend: 

• State Estimation for Robotics (by Tim Barfoot et. al. 2025)

• Probabilistic Robotics (by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press, 2005) as a supplement to the lectures.

Class Format 

• Each week there will be single lecture slot, i.e. Tuesdays, 11:00 AM – 01:30 PM. 

• Students will also have lab assignments to be completed on their own timeline.

Grading Breakdown

• Projects – 35%

• Labs – 45%

• Midterm Exam – 20%

Late Work Policy

You will have 3 late days to use on labs throughout the semester. These 3 days (not hours) can be split among the labs. Students do not need to discuss or report late days with the instructor, (i.e. just submit your reports and we will sum up late days at the end of the semester). No late work is accepted for project presentations. 

Inclusion Statement

The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs, ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations, and abilities will be treated with respect.   It is my intent that all students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit.  If this standard is not being upheld, please feel free to speak with me.

Moses Center Statement of Disability

If you are student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities (CSD) at 212-998-4980 or mosescsd@nyu.edu.  You must be registered with CSD to receive accommodations.  Information about the Moses Center can be found at www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 3rd floor.

NYU School of Engineering Policies and Procedures on Academic Misconduct

1. Introduction: The School of Engineering encourages academic excellence in an environment that promotes honesty, integrity, and fairness, and students at the School of Engineering are expected to exhibit those qualities in their academic work. It is through the process of submitting their own work and receiving honest feedback on that work that students may progress academically. Any act of academic dishonesty is seen as an attack upon the School and will not be tolerated. Furthermore, those who breach the School’s rules on academic integrity will be sanctioned under this Policy. Students are responsible for familiarizing themselves with the School’s Policy on Academic Misconduct.

2. Definition: Academic dishonesty may include misrepresentation, deception, dishonesty, or any act of falsification committed by a student to influence a grade or other academic evaluation. Academic dishonesty also includes intentionally damaging the academic work of others or assisting other students in acts of dishonesty. Common examples of academically dishonest behavior include, but are not limited to, the following:

1. Cheating: intentionally using or attempting to use unauthorized notes, books, electronic media, or electronic communications in an exam; talking with fellow students or looking at another person’s work during an exam; submitting work prepared in advance for an in-class examination; having someone take an exam for you or taking an exam for someone else; violating other rules governing the administration of examinations.

2. Fabrication:  including but not limited to, falsifying experimental data and/or citations.

3. Plagiarism: intentionally or knowingly representing the words or ideas of another as one’s own in any academic exercise; failure to attribute direct quotations, paraphrases, or borrowed facts or information. 

4. Unauthorized collaboration: working together on work that was meant to be done individually.

5. Duplicating work: presenting for grading the same work for more than one project or in more than one class, unless express and prior permission has been received from the course instructor(s) or research adviser involved. 

6. Forgery: altering any academic document, including, but not limited to, academic records, admissions materials, or medical excuses.

If you are experiencing an illness or any other situation that might affect your academic performance in a class, please email Deanna Rayment, Coordinator of Student Advocacy, Compliance and Student Affairs: deanna.rayment@nyu.edu.  Deanna can reach out to your instructors on your behalf when warranted.