MCL in Action

MCL vs EKF in Action

At time :

t=1 , Particles are drawn randomly and uniformly over the entire pose space.
t=2 , Measurement is updated and an importance weight is assigned to each particle.
t=3 , Motion is updated and a new particle set with uniform weights and high number of particles around the three most likely places is obtained in resampling.
t=4 , Measurement assigns non-uniform weight to the particle set.
t=5 , Motion is updated and a new resampling step is about to start.

At time :

t=1 , Initial belief represented by a Gaussian distribution around the first door.
t=2 , Motion is updated and the new belief is represented by a shifted Gaussian of increased weight.
t=3 , Measurement is updated and the robot is more certain of its location. The new posterior is represented by a Gaussian with a small variance.
t=4 , Motion is updated and the uncertainty increases.

Select concepts that are common to both the MCL and EKF algorithms

Particle filters

Gaussian distribution

Resampling stage

Assigning importance weights

Motion and measurement stages

Non-Gaussian distribution

SOLUTION:

SOLUTION: