Course update: Lectures 1–5 notes published; revised outline; Lecture 6 next week

[yangyulin]

Halfway there! 🎉

Lectures 1–5 are done, and the typeset notes for all five are published in a single accumulating document: rise_notes.pdf (one section per lecture, 57 pages).

Newly added: Lecture 5 — the IMU model and IMU integration (IMU/noise modeling, exact integration, preintegration — discrete on-manifold vs. analytic ACI²/CPI — and mean/covariance propagation with SO(3) and SE₂(3) error parametrizations).

Revised outline

The back half of the course has been reorganized — see the README for the full table:

#	Topic	Instructor	Status
1	Basics: probability, estimator, linear system	Yulin Yang	✅
2	3D geometry: rotation, Lie group (SE(3), SE₂(3))	Yulin Yang	✅
3	Visual SLAM I: camera model, point/line/plane	Yulin Yang	✅
4	Visual SLAM II: information, marginalization and pipeline	Yulin Yang	✅
5	IMU model and integration	Yulin Yang	✅
6	Kalman filter (KF) and observability analysis, KF-based VINS	Yulin Yang	next week
7	IMU pre-integration based VINS, multi-visual-inertial	Yulin Yang	todo
8	State formulation (global-centric, invariant, robocentric, equivariant…)	Chuchu Chen	todo
9	Graph marginalization and sparsification, C-KLAM	Chuchu Chen	todo
10	Learning-based SLAM / Dense Mapping	Xingxing Zuo	todo

Up next: Lecture 6 — Kalman filter and observability analysis, KF-based VINS (Yulin Yang), next week.

Questions on any lecture are welcome in the Q&A category.