Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

TL;DR

This paper introduces an online probabilistic framework that combines visual and tactile data using Gaussian Random Fields and Gaussian Process Implicit Surfaces to actively explore and model 3D environments with a robot.

Contribution

It presents a novel method for integrating visual and tactile sensing for active exploration and environment modeling using Gaussian processes and random fields.

Findings

Successfully explored incomplete point clouds to identify regions of interest.

Demonstrated effective object detection and terrain classification.

Validated with experiments using a robotic arm and various sensors.

Abstract

In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demonstrate how to use the online framework for object detection and terrain classification.