====== 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
    *