  This paper studies the problem of detection and tracking of general objects
with long-term dynamics, observed by a mobile robot moving in a large
environment. A key problem is that due to the environment scale, it can only
observe a subset of the objects at any given time. Since some time passes
between observations of objects in different places, the objects might be moved
when the robot is not there. We propose a model for this movement in which the
objects typically only move locally, but with some small probability they jump
longer distances, through what we call global motion. For filtering, we
decompose the posterior over local and global movements into two linked
processes. The posterior over the global movements and measurement associations
is sampled, while we track the local movement analytically using Kalman
filters. This novel filter is evaluated on point cloud data gathered
autonomously by a mobile robot over an extended period of time. We show that
tracking jumping objects is feasible, and that the proposed probabilistic
treatment outperforms previous methods when applied to real world data. The key
to efficient probabilistic tracking in this scenario is focused sampling of the
object posteriors.
