Task-driven active sensing framework applied to leaf probing

Abstract

This article presents a new method for actively exploring a 3D workspace with the aim of localizing relevant regions for a given task. Our method encodes the exploration route in a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum-information-gain gathering approach, incrementally provide a better understanding of the scene until reaching the task termination criterion. This approach is designed to be applicable to any task entailing 3D object exploration where some previous knowledge of its approximate shape is available. Its suitability is demonstrated here for a leaf probing task using an eye-in-hand arm configuration in the context of a phenotyping application (leaf probing).