Module Number

INFO-4390 		Module Title

Visual Perception and Learning for Robotics 		Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)
Content

- Introduction to robot types and sensors
- Image formation
- Two-view geometry
- Deep learning basics
- Image motion and optical flow
- Keypoints and descriptors
- Camera motion estimation
- Probabilistic state estimation
- Visual simultaneous localization and mapping
- Visual-inertial odometry
- Event-based vision
- 3D object detection and tracking
- Learning-based planning and control from images
Objectives

(1) Students can phrase a robotic visual perception problem as algebraic, probabilistic state estimation or machine learning problem and can select an appropriate algorithm for solving it.

(2) Students are able to implement a set of robotic visual perception and learning algorithms and analyze their behavior.

(3) Students can explain the challenges in robotic visual perception and learning and assess and characterize new methods.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
2
3.0
wt
90
g
100
Tutorial
Ü
o
2
3.0
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Stückler
Literature

Recommended to attend deep learning course before. Basic programming skills in python required.

Lecture slides will be provided. Further literature will be announced in the lecture. Recommended textbooks:

- An Invitation to 3-D Vision by Yi Ma, Stefano Soatto, Jana Košecká, S. Shankar Sastry

- R. Szeliski's book on Computer Vision: Algorithms and Applications

- K. Murphy's book on Machine Learning: A Probabilistic Perspective

- Deep Learning by Goodfellow, Bengio and Courville
https://www.deeplearningbook.org
Last offered Wintersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV