Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems
Bhattacharjee P, Burger M, Boerner A, Morgenshtern V (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Abstract
Goal oriented autonomous operation of space rovers has been known to increase scientific output of a mission. In this work we present an algorithm, called the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs) in an exploration scenario in order to utilise the limited resources of the imaging instrument on the rover effectively. This prioritisation is based on an estimator that evaluates the change in information content at consecutive spatial scales of the RoIs without calculating the finer scale reconstruction. The estimator, called the Refinement Indicator (RI), is motivated and derived. Multi-scale acquisition approaches, based on classical and multi-level compressed sensing, with respect to the single pixel camera architecture are discussed. The performance of the algorithm is verified on remote sensing images and compared with the state-of-the-art multi-resolution reconstruction algorithms. At the considered sub-sampling rates the RPS is shown to better utilise the system resources for reconstructing the RoIs.
Authors with CRIS profile
Martin Burger
Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)
Veniamin Morgenshtern
Professur für Maschinelles Lernen in der Signalverarbeitung
Involved external institutions
Germany (DE)How to cite
APA:
Bhattacharjee, P., Burger, M., Boerner, A., & Morgenshtern, V. (2022). Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2022.3163552
MLA:
Bhattacharjee, Protim, et al. "Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems." IEEE Transactions on Computational Imaging (2022).
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