Table of Contents
Prof. talk: Mobile Robot
Headline
Lehr probe
Prof. talk: Mobile Robot
gap b7t state estimation and cognition path & planning
deep reinforcement learning
Modle vs Learning
Headline
Probabilitic SKAM formuation
research: decide (map representation) → model (likelihood) → choose ML/MAP problem → find
state: velocity (phy), sensors,
Map representation
depth map, truncated sidgned disance function,
Multi-sensor fusion
visual only vs visual inertial
nonlin. least sq.
cost: reprojections errors, diff 2D keypoints and 3D projection
OKVIS, open source
Semantic SLAM
Semantic fusion
Input → CNN
semantic texture for dense tracking
Learning for SLAM
codeSLAM
Training data set
Future: autnoomous drone with intuitive user interface
Lehr probe
Ebene von robot intelligent
Moravec's paradox
wissen & planning: enable introspection, shared representation of map, knowledge
gap problem: limited robustness
deep learning
verhalten, regelung durch contruction
Regelungsteknik SISO
SISO, MIMO
proportional intergral differenzial regler
time diffrentiated measurement
Anticipation & simulation
PID regler extension
reference filter
anti-reset-windup
pre-control