  In this work, we present a method for tracking and learning the dynamics of
all objects in a large scale robot environment. A mobile robot patrols the
environment and visits the different locations one by one. Movable objects are
discovered by change detection, and tracked throughout the robot deployment.
For tracking, we extend the Rao-Blackwellized particle filter of previous work
with birth and death processes, enabling the method to handle an arbitrary
number of objects. Target births and associations are sampled using Gibbs
sampling. The parameters of the system are then learnt using the Expectation
Maximization algorithm in an unsupervised fashion. The system therefore enables
learning of the dynamics of one particular environment, and of its objects. The
algorithm is evaluated on data collected autonomously by a mobile robot in an
office environment during a real-world deployment. We show that the algorithm
automatically identifies and tracks the moving objects within 3D maps and
infers plausible dynamics models, significantly decreasing the modeling bias of
our previous work. The proposed method represents an improvement over previous
methods for environment dynamics learning as it allows for learning of fine
grained processes.

Robotics