Efficient 3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning

Abstract

3D reconstruction is a fundamental task in robotics that has gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. While this task can be carried out using a large number of arbitrarily taken 2D images, their processing may become laborious, time-consuming, and in some instances may not provide the necessary information about the object of interest. An efficient alternative is the so-called view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information. Nonetheless, in most real-world settings, existing environmental noise can significantly affect the performance of 3D reconstruction. To that end, this work advocates a novel geometric-based reconstruction quality function for VP, that accounts for the existing noise of the environment, without requiring its closed-form expression. With no analytic expression of the objective function, this work puts forth an adaptive Bayesian optimization algorithm for accurate 3D reconstruction in the presence of noise. Numerical tests on simulated and real noisy agricultural environments showcase the merits of the proposed VP approach for efficient 3D reconstruction with even a small number of available cameras.