8 research outputs found
Variational Bayesian Expectation Maximization for Radar Map Estimation
For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters
Variational Bayesian Expectation Maximization for Radar Map Estimation
Abstract-For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
This paper addresses the mapping problem. Using a conjugate prior form, we
derive the exact theoretical batch multi-object posterior density of the map
given a set of measurements. The landmarks in the map are modeled as extended
objects, and the measurements are described as a Poisson process, conditioned
on the map. We use a Poisson process prior on the map and prove that the
posterior distribution is a hybrid Poisson, multi-Bernoulli mixture
distribution. We devise a Gibbs sampling algorithm to sample from the batch
multi-object posterior. The proposed method can handle uncertainties in the
data associations and the cardinality of the set of landmarks, and is
parallelizable, making it suitable for large-scale problems. The performance of
the proposed method is evaluated on synthetic data and is shown to outperform a
state-of-the-art method.Comment: 14 pages, 6 figure
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019
Dieser Tagungsband enthält die Beiträge des 29. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen