Semantische 3D-Perzeption für robotische Systeme

Dauer4 SWS
ArtVorlesung mit Übung
VortragendeDr. Ing. Michael Suppa
ÜbungsleitungFerenc Balint-Benczedi,Jan-Hendrik Worch
SpracheDeutsch und English
Termine Vorlesung und Übungen19.10.2017-01.02.2018 10:00-14:00
Ort TAB-Gebäude (Am Fallturm 1), Eingang E, Raum 0.31
Wichtige BemerkungenCourse and exercise alternate. Check Schedule below

Pleas sign up for the course on StudIP


Dr. Michael Suppa is a leading expert in robot perception and visiting professor at the University of Bremen. He worked as project manager and researcher at the Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) in the research areas: robotic exploration, 3-D vision, and data fusion. From 2009 until 2015, he was the Head of the Department Perception and Cognition, a recognized world leader on key robotic research topics such as complex scene analysis, perception for resource-limited systems and robotic cognition. In March 2015 he co-founded Roboception, a DLR spin-off company devoted to advancing the State-of-the -Art in 3D sensors and vision.

The lecture covers all important aspects of 3D semantic perception with a high focus on applicability and real world tasks. Some of the topics covered by the lecture:

  • Introduction to 3D
  • Features and Descriptors
  • From features to semantics
  • Scene registration – RGBD SLAM
  • Handling uncertainty in 3D
  • Perception Systems
  • Convolutional Neural Networks
  • and more

Assignments Schedule:

Assignment nr.Handed outSubmission date
A126th October13th November 08:00 AM
A216th November4th December 08:00 AM
A37th December8th January 08:00 AM
A411th January4th February 12:00 PM

Three out of the four assignments must be submitted on time to enter to oral exam. Assignments will not be graded, but they will form a basis for discussion during the oral exam

Course Schedule (subject to change):

The order and date of exercises is subject to change. Check back regularly.

19.10 10:00-11:30 and 12:30-14:00Lecture: Introduction to 3D and Acquisition of 3D data
26.10 10:00-11:30Exercise: Introduction to 3D
09.11 10:00-11:30 and 12:30-14:00 Lecture: Features and Filters
16.11 10:00-11:30Exercise: 2D Keypoints and descriptors
23.11 10:00-11:30Exercise: 3D Keypoints and descriptors
30.11 10:00-11:30 and 12:30-14:00 Lecture: Objects I and II
07.12 10:00-11:30Exercise: Object recognition
14.12 10:00-11:30Exercise: Discussion of assignment
21.12 10:00-11:30 and 12:30-14:00 Lecture: Scenes I and II
11.01 10:00-11:30Exercise: Segmentation of kitchen scene
18.01 10:00-11:30 and 12:30-14:00 Lecture: CNNs and Applications
25.01 10:00-11:30Exercise: Exercise using CNNs