Table of Contents
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
