Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
Paper in 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

Royal Institute of Technology (KTH)

Yasemin Bekiroglu

University of Birmingham

Carl Henrik Ek

Royal Institute of Technology (KTH)

Danica Kragic

Royal Institute of Technology (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,

Subject Categories

Other Computer and Information Science


Computer Vision and Robotics (Autonomous Systems)



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