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

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.

Automated summary

In this work, a RoI Prioritised Sampling algorithm is proposed to effectively utilize the limited resources of the imaging instrument on a space rover. The algorithm prioritizes Region-of-Interests (RoIs) based on an estimator that evaluates the change in information content at consecutive spatial scales. The algorithm's performance is compared with state-of-the-art multi-resolution reconstruction algorithms and shown to better utilize the system resources.