Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

Sergio Caccamo, Yasemin Bekiroglu, Carl Henrik Ek, Danica Kragic

Research output: Contribution to journalArticle (Academic Journal)

17 Citations (Scopus)

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.
Original languageUndefined/Unknown
JournalarXiv
DOIs
Publication statusPublished - 13 Feb 2018

Bibliographical note

8 pages, 6 figures, external contents (https://youtu.be/0-UlFRQT0JI)

Keywords

  • cs.RO

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