Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems

TL;DR

This paper introduces the RoI Prioritised Sampling (RPS) algorithm, which enhances autonomous space exploration by efficiently prioritizing regions of interest using a novel estimator, improving resource utilization for image reconstruction.

Contribution

The paper presents a new RPS algorithm with the Refinement Indicator estimator for better resource allocation in autonomous exploration, validated on remote sensing images.

Findings

RPS outperforms existing multi-resolution algorithms at sub-sampling rates.

RPS effectively prioritizes regions of interest, improving reconstruction quality.

The estimator accurately evaluates information change without fine-scale reconstruction.

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 multilevel 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.