An Information Gain Formulation for Active Volumetric 3D Reconstruction

Abstract

We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.