Take Your Best Shot: Sampling-Based Planning for Autonomous Photography

Abstract

Autonomous mobile robots (AMRs) equipped with high-quality cameras are revolutionizing the field of autonomous photography by delivering efficient and cost-effective methods for capturing dynamic visual content. As AMRs are deployed in increasingly diverse environments, the challenge of consistently producing high-quality photographic content remains. Traditional approaches often involve AMRs following a predetermined path while capturing data-intensive imagery, which can be suboptimal, especially in environments with limited connectivity or physical obstructions. These drawbacks necessitate intelligent decision-making to pinpoint optimal vantage points for image capture. Inspired by Next Best View studies, we propose a novel autonomous photography framework that enhances image quality and minimizes the number of photos needed. This framework incorporates a proposed evaluation metric that leverages ray-tracing and Gaussian process inter-polation, enabling the assessment of potential visual information from the target in partially known environments. A derivative-free optimization (DFO) method is then proposed to sample candidate views and identify the optimal viewpoint. The effectiveness of our approach is demonstrated by comparing it with existing methods and further validated through simulations and experiments with various vehicles. Note–Code and videos of the simulations and experiments are provided in the supplementary material and can be accessed at https://www.bezzorobotics.com/sg-lb-icra25.