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Anytime Planning for Decentralized Multirobot Active Information Gathering
Regional variance for multi-object filtering
Recent progress in multi-object filtering has led to algorithms that compute
the first-order moment of multi-object distributions based on sensor
measurements. The number of targets in arbitrarily selected regions can be
estimated using the first-order moment. In this work, we introduce explicit
formulae for the computation of the second-order statistic on the target
number. The proposed concept of regional variance quantifies the level of
confidence on target number estimates in arbitrary regions and facilitates
information-based decisions. We provide algorithms for its computation for the
Probability Hypothesis Density (PHD) and the Cardinalized Probability
Hypothesis Density (CPHD) filters. We demonstrate the behaviour of the regional
statistics through simulation examples
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
Riemann-Langevin Particle Filtering in Track-Before-Detect
Track-before-detect (TBD) is a powerful approach that consists in providing
the tracker with sensor measurements directly without pre-detection. Due to the
measurement model non-linearities, online state estimation in TBD is most
commonly solved via particle filtering. Existing particle filters for TBD do
not incorporate measurement information in their proposal distribution. The
Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to
exploit all available knowledge of the posterior (that is, both prior and
measurement information). This letter synthesizes recent advances in LMC-based
filtering to describe the Riemann-Langevin particle filter and introduces its
novel application to TBD. The benefits of our approach are illustrated in a
challenging low-noise scenario.Comment: Minor grammatical update
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