7 accepted papers
Sparse Bayesian Learning with dictionary refinement (SBL-DR) is a gridless technique for sparse signal reconstruction, focusing on super-resolution estimation of spectral line locations and their quantity. Its cost function coinsides with that of stochastic maximum likelihood (SML), a well-known met…
We present a 3D seismic exploration and imaging survey conducted by robotic platforms in a hardware-in-the-loop system. To this end, we integrate the adapt-then-combine full waveform inversion (ATC-FWI) over a network of mobile rovers in the ROS2 framework. The ATC-FWI allows for distributed subsurf…
We present a parallel implementation of a 2D seismic ray tracer on a graphics processing unit of the compact Jetson Nano by Nvidia. Ray tracing is commonly used in seismic imaging as an intermediate step in reconstructing subsurface structures. We employ a gradient-based ray tracer that requires a t…
We consider the problem of distributed subsurface imaging in seismic receiver networks. This problem is particularly relevant for future planetary exploration missions where multi-agent networks shall autonomously reconstruct a subsurface based on network-wide measurements. To this end, we propose a…
In processing spatially distributed data, multi-agent robotic platforms equipped with sensors and computing capabilities are gaining interest for applications in inhospitable environments. In this work an algorithm for a distributed realization of sparse bayesian learning (SBL) is discussed for lear…
Information gathering algorithms aim to intelligently select the robot actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for information gathering. However, these algorith…
Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi-ag