Online Informative Path Planning for Active Information Gathering of a 3D Surface

TL;DR

This paper introduces an online path planning method for aerial robots to efficiently gather information on 3D surfaces by leveraging manifold Gaussian processes, outperforming traditional coverage methods in accuracy and efficiency.

Contribution

It presents a novel online informative path planning approach using manifold Gaussian processes with geodesic kernels for active surface information gathering.

Findings

Outperforms traditional coverage and random exploration in accuracy.

Utilizes manifold Gaussian processes to incorporate spatial correlations.

Improves information gathering efficiency significantly.

Abstract

This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.