Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
Paper i proceeding, 2016

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 demostrate how to use the online framework for object detection and terrain classification.

Surface reconstruction

Surface treatment

Robot sensing systems

Three-dimensional displays

Gaussian processes



Sergio Caccamo

Kungliga Tekniska Högskolan (KTH)

Yasemin Bekiroglu

University of Birmingham

Carl Henrik Ek

Kungliga Tekniska Högskolan (KTH)

Danica Kragic

Kungliga Tekniska Högskolan (KTH)

IEEE International Conference on Intelligent Robots and Systems

2153-0858 (ISSN)

978-150903762-9 (ISBN)

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Daejeon, South Korea,


Annan data- och informationsvetenskap

Robotteknik och automation

Datorseende och robotik (autonoma system)



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