2 research outputs found
Detection and Tracking of General Movable Objects in Large 3D Maps
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.Comment: Submitted for peer revie
Multiple Object Detection, Tracking and Long-Term Dynamics Learning in Large 3D Maps
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.Comment: Submitted for peer revie