69 research outputs found
Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach
Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Today, the most widespread, widely applicable technology for gathering data
relies on experienced scientists armed with handheld radio telemetry equipment
to locate low-power radio transmitters attached to wildlife from the ground.
Although aerial robots can transform labor-intensive conservation tasks, the
realization of autonomous systems for tackling task complexities under
real-world conditions remains a challenge. We developed ConservationBots-small
aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial
robot achieves robust localization performance and fast task completion times
-- significant for energy-limited aerial systems while avoiding close
encounters with potential, counter-productive disturbances to wildlife. Our
approach overcomes the technical and practical problems posed by combining a
lightweight sensor with new concepts: i) planning to determine both trajectory
and measurement actions guided by an information-theoretic objective, which
allows the robot to strategically select near-instantaneous range-only
measurements to achieve faster localization, and time-consuming sensor rotation
actions to acquire bearing measurements and achieve robust tracking
performance; ii) a bearing detector more robust to noise and iii) a tracking
algorithm formulation robust to missed and false detections experienced in
real-world conditions. We conducted extensive studies: simulations built upon
complex signal propagation over high-resolution elevation data on diverse
geographical terrains; field testing; studies with wombats (Lasiorhinus
latifrons; nocturnal, vulnerable species dwelling in underground warrens) and
tracking comparisons with a highly experienced biologist to validate the
effectiveness of our aerial robot and demonstrate the significant advantages
over the manual method.Comment: 33 pages, 21 figure
A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
State space models in which the system state is a finite set--called the
multi-object state--have generated considerable interest in recent years.
Smoothing for state space models provides better estimation performance than
filtering by using the full posterior rather than the filtering density. In
multi-object state estimation, the Bayes multi-object filtering recursion
admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
the multi-object posterior. We also propose an implementation of this so-called
multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
implementation
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
Generalized Labeled Multi-Bernoulli (GLMB) densities arise in a host of
multi-object system applications analogous to Gaussians in single-object
filtering. However, computing the GLMB filtering density requires solving
NP-hard problems. To alleviate this computational bottleneck, we develop a
linear complexity Gibbs sampling framework for GLMB density computation.
Specifically, we propose a tempered Gibbs sampler that exploits the structure
of the GLMB filtering density to achieve an complexity,
where is the number of iterations of the algorithm, and are the
number hypothesized objects and measurements. This innovation enables an
complexity implementation of the GLMB filter.
Convergence of the proposed Gibbs sampler is established and numerical studies
are presented to validate the proposed GLMB filter implementation
- …