In this paper we focus on the task of summarizing
observations made by a mobile robot on a trajectory. A
navigation summary is the synopsis of these observations. We
pose the problem of generating navigation summaries as a
sampling problem. The goal is to select a few samples from
the set of all observations, which are characteristic of the
environment, and capture its mean properties and surprises.
We define the surprise score of an observation as its distance
to the closest sample in the summary. Hence, an ideal summary
is defined to have a low mean and a low max surprise score,
measured over all the observations.
We present three different strategies for solving this sampling
problem. Of these, we show that the kCover sampling algorithm
produces summaries with low mean and max surprise scores;
even in the presence of noise. These results are demonstrated
on datasets acquired in different robotics context.