Lectures, 2026 spring:

• Wednesdays 10:15-11:45 , 2-219 Graphics Lab (South Building)
• Wednesdays 14:15-15:45 , 0-222 (South Building)

Teachers:

• Peter Kozma
• Levente Hajder

---

Peter Kozma’s lecture slides:

• Lecture #1: Self-driving Future
• Lecture #2: Camera Sensors
• Lecture #3: LiDAR Sensors
• Lecture #4: Radar Sensors
• Lecture #5: GNSS-INS Sensors

Agenda

Week	Tuesday
1st: Registration week
2nd	Introduction
3rd
4th
5th
6th
7th
8th
9th
10th
11th
12th
13th
14th

Exams

• TDL

---

Agenda, 2025 fall – obsolete

Week	Tuesday
1st: Registration week
2nd	Introduction
3rd	Estimation theory: inhomogeneous linear systems, homogeneous sytems, circle estimation
4th	LiDAR-Camera calibration using a spherical target
5th	LiDAR-Camera calibration using a spherical target (cont)
6th	Lagrange multilpliers. Ellipse estimation
7th	Point set registration
8th – fall break
9th	Optimal point-line distance. Point-ellipse distance.
10th	Robust fitting: the RANSAC method. Iterative & Weighted Least-Squares Estimation
11th	Camera calibration. LiDAR-camera calibration via chessboard patterns.
12th	Demo on MATLAB LiDAR-Camera calibration. Data for Matlab
13th	Barycentric corrdinates (slides 32-41). EPnP algorithm
14th	Rodrigues formula. Application of Cayley parameters.

---

Exams

• TDL

2024 fall

Agenda

Week	Tuesday
1st: Registration week
2nd	Introduction
3rd	Estimation theory: inhomogeneous linear systems, homogeneous sytems, circle estimation
4th	LiDAR-Camera calibration using a spherical target
5th	Point set registration
6th	ByAhadliTartlan
7th	ByAhadliTartlan
8th	LiDAR-Camera MATLAB toolbox using chessboards
9th – fall break
10th	ByAhadliTartlan
11th	ByAhadliTartlan
12th	Lie theory
13th	Lie theory (cont.)
14th	Point-ellipse distance. Iterative & Weighted Least-Squares Estimation

Peter Kozma’s lecture slides:

• Lecture #1: Self-driving Future
• Lecture #2: Camera Sensors
• Lecture #3: LiDAR Sensors
• Lecture #4: Radar Sensors
• Lecture #5: GNSS-INS Sensors

Exams

• 18th December 11:00
• 3rd January 16:00 (room 0-409, South Building)
• 17th January 16:00 (room 2-502, South Building)
• 31st January 16:00 (room 0-412, South Building)

Topics for oral exam (preliminary)

At the oral exam, two topics will be randomly selected.

Qestions from Peter Kozma:

1. Self-driving future.
2. Camera.
3. LiDAR.
4. RADAR.
5. GNSS-INS.

Questions from Levente Hajder

1. Circle and sphere fitting.
2. Optimal line and plane fitting.
3. Ellipse fitting on camera images.
4. Calculation of point-line and point-ellipse distances.
5. Robust estimation: the RANSAC algorithm
6. Robust eszimation: application of the L1 norm; the iterative reweighted least squares algorithm.
7. Optimal point registration algorithm.
8. Sphere center estimation from camera images via ellipse detection.
9. Lidar-camera calibration via a spherical target. Pose (rotation+translation) estimation between a camera and a LiDAR device.
10. Lidar-camera calibration using chessboards.
11. The Effective Perspective n Point (EPnP) method.
12. Representations of a rotation: orthogonal matrices, Rodrigues formula, Cayley parameters.
13. Lie theory – SO(3): Describe the relation of skew symmetric matrices, infinitesimal rotation. How to derive the exponential map. Closed from for the exponential and logarithmic maps. Alternative representations of 3D rotation.
14. Lie theory – SE(3): Describe & define notable linear groups (GL(n) etc.). Representation of rigid motion, Exp, Log. Application(s) of SE(3).
15. Lie theory – Applications: Describe the relation of a Lie group and its corresponding Lie algebra. Why is the Identity element of the group so important? Exp, Log, boxplus, boxminus. Interpolation. Uncertain 3D rotation, composition…

Final grade

The final grade is the sum of oral exam (max. 25+25=50) and assignment scores. At least 20 points should be reached both from oral exam and assignment scores.

Thresholds for marks as follows:

• Excellent (5): >=85
• Good (4): 70-84
• Satisfactory (3): 55-69
• Pass (2): 40-54
• Fail (1): 0-39