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Filip Bártek

Monte Carlo localization - Wikipedia, the free encyclopedia - 0 views

  • particle filter localization
  • typically starts with a uniform random distribution of particles
  • hopefully most particles will converge to where the robot actually is.
  • ...19 more annotations...
  • environment is static and does not change with time
    • Filip Bártek
       
      We also need to capture the robot's velocity in the state (pose) in case the robot has some momentum, so the three parameters may be not enough for a 2D robot.
  • assumes the Markov property
  • position and orientation
  • particles are uniformly distributed over the configuration space
  • Given a map
  • every time
  • every time
  • motion_update
  • sensor_update
  • sensor_update
  • some noise is applied
  • It now believes it is at one of two locations.
  • The robot has successfully localized itself.
  • actuation command
  • no actuator is perfect: they may overshoot or undershoot the desired amount of motion
  • the motion model must be designed to include noise as necessary
  • Particles which were consistent with sensor readings are more likely to be chosen
  • possibly more than once
  • a robot becomes increasingly sure of its position as it senses its environment
Filip Bártek

Recursive Bayesian estimation - Wikipedia, the free encyclopedia - 0 views

  • is constant relative to
  • is constant relative to
  • is constant relative to
  • ...13 more annotations...
  • Markov assumption
  • conditionally independent of the other earlier states
  • measurement at the k-th timestep is dependent only upon the current state
  • proportional
    • Filip Bártek
       
      It is proportional with the factor \alpha. \alpha = 1 / p(z_k|z_{1:k-1}) A way to compute the value of \alpha is shown below. It is common to all the updated states x_k at a given time k and measurement z_k.
  • predicted
  • marginalising out the previous states
  • predicted state
    • Filip Bártek
       
      p(x_k|z_{1:k-1})
  • update
  • predict and update steps
  • measurement likelihood
    • Filip Bártek
       
      p(z_k|x_k)
  • is constant relative to
  • can usually be ignored in practice
    • Filip Bártek
       
      We are typically interested in relative probabilities of the states. Equivalently [?], the p(x_k|z_{1:k}) across all the estimated states x_k is a probability distribution: \Sum_{x_k}{p(x_k|z_{1:k})} = 1
  • simply normalized, since its integral must be unity
Astro Biology

MSH 11-62 Supernova Remnant - 0 views

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    Chandra of the supernova remnant MSH 11-62 reveals an irregular shell of hot gas, shown in red, surrounding an extended nebula of high energy X-rays, shown in blue.
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Astro Biology

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