Stein Coverage: A Variational Inference Approach to Distribution-Matching Multisensor Deployment

This letter addresses a spatial coverage optimization problem where multiple heterogeneous sensors are deployed in a convex environment with a known area priority function. Each sensor's coverage is defined by an anisotropic spatial distribution. We introduce the Stein Coverage algorithm, a dis...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2024-06, Vol.9 (6), p.5370-5376
Main Authors: Ghimire, Donipolo, Kia, Solmaz S.
Format: Article
Language:English
Subjects:
Algorithms
Coverage
Distribution functions
Graphical models
Inference
Matching
multisensor deployment
optimal transport
Optimization
Quality of service
Robot sensing systems
Sampling methods
Sensor phenomena and characterization
Sensors
Spatial distribution
variational inference
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Summary:This letter addresses a spatial coverage optimization problem where multiple heterogeneous sensors are deployed in a convex environment with a known area priority function. Each sensor's coverage is defined by an anisotropic spatial distribution. We introduce the Stein Coverage algorithm, a distribution-matching coverage approach that aims to place sensors at positions and orientations that result in a collective coverage distribution that is as close as possible to the event distribution. To select the most important representative points from the coverage event distribution, Stein Coverage utilizes the Stein Variational Gradient Descent (SVGD), a deterministic sampling method from the variational inference literature. An innovation in our work is the introduction of a repulsive force between the samples in the SVGD algorithm to spread the samples and avoid footprint overlap for the deployed sensors. After pinpointing the points of interest for deployment, Stein Coverage solves the multisensor assignment problem using a bipartite optimal matching process. Simulations demonstrate the advantages of the Stein Coverage method compared to conventional Voronoi partitioning multisensor deployment methods.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3390541