Welcome to RiSE (Robotics & State Estimation) Tutorial

[yangyulin]

Robotics & State Estimation (RiSE) Tutorials

Philosophy

Math lasts forever, even in the AI era.

The best way to attack Math is to write it down and think twice yourself.

Motivation

This course is designed to cover the in-depth math knowledge essential for research in Robotics & State Estimation, with direct applications to:

• Robot perception and navigation
• Simultaneous Localization and Mapping (SLAM)
• Structure from Motion (SfM)
• Visual-Inertial Odometry (VIO)
• Multi-sensor fusion & calibration

Outline

#	Topic	Instructor
1	Basics: probability, linear algebra, linear systems	Yulin Yang
2	Rotation, Lie / quaternion, translation, SE(3), SE2(3)	Yulin Yang
3	Camera model, triangulation, calibration, point / line / plane	Yulin Yang
4	IMU model, IMU integration	Yulin Yang
5	Kalman filter and batch least squares, marginalization, null space	Yulin Yang
6	State formulation (global-centric, invariant, robocentric, equivariant…)	Chuchu Chen
7	Kalman-filter-based VINS, optimization-based, observability analysis	Chuchu Chen
8	IMU pre-integration-based VINS, multi-IMU	Yulin Yang
9	Learning-based SLAM / Dense Mapping	Xingxing Zuo

Course Logistics

• One lecture per week.
• Bring a pen and paper — follow along with the equation derivations.
• Video recordings and notes are shared after each lecture. Please do not redistribute (e.g., to YouTube) for now.
• Discussions and office hours are held on GitHub: https://github.com/yangyulin/rise-tutorial/discussions

Course Time

Weekly on Sunday:

Location	Local time
Seattle	6:30 AM – 7:45 AM PDT
Washington DC	9:30 AM – 10:45 AM EDT
Abu Dhabi	5:30 PM – 6:45 PM
Beijing	9:30 PM – 10:45 PM

Contact

• Yulin Yang — yangyulin1@gmail.com
• Chuchu Chen — chuchu.chen@gwu.edu
• Xingxing Zuo — xingxing.zuo@mbzuai.ac.ae