After the resampling wheel algorithm makes one full rotation around the wheel, every particle has been given a chance to be selected.

When using the online version of Graph SLAM, it is possible for future measurements of landmarks to update the estimate of a past robot pose

In a Kalman filter, the Gaussian distribution of measurement noise is assumed to have:

Assume a Gaussian distribution is used to calculate importance weights in a particle filter.  Currently, particle A has the highest importance weight.  Which particle will have the highest importance weight if both mu and sigma are increased by 50%?

In a situation where a robot is navigating from a known starting location to a fixed ending position with no movement noise, the most computationally efficient path planning algorithm to use is:

Given y=.  What value of x makes the probability distribution in this grid world valid?

The particle with the highest weight is always sampled.

The ozone layer absorbs ultraviolet radiation and helps to warm the stratosphere.

Particle A has greater weight than particle B as shown in the figure below.  If we use the resampling wheel to resample these two particles with respect to their weights, which of the following sampling of particles is most likely?   

Which of the following substances would be the MOST acidic?