Machine Learning

, Volume 94, Issue 1, pp 3–23 | Cite as

Plane-based object categorisation using relational learning

Article

Abstract

We use Inductive Logic Programming (ILP) to learn classifiers for generic object recognition from point clouds, as generated by 3D cameras, such as the Kinect. Each point cloud is segmented into planar surfaces. Each subset of planes that represents an object is labelled and predicates describing those planes and their relationships are used for learning. Our claim is that a relational description for classes of 3D objects can be built for robust object categorisation in real robotic application. To test the hypothesis, labelled sets of planes from 3D point clouds gathered during the RoboCup Rescue Robot competition are used as positive and negative examples for an ILP system. The robustness of the results is evaluated by 10-fold cross validation. In addition, common household objects that have curved surfaces are used for evaluation and comparison against a well-known non-relational classifier. The results show that ILP can be successfully applied to recognise objects encountered by a robot especially in an urban search and rescue environment.

Keywords

Object classification Inductive logic programming Machine learning Urban search and rescue 3D point cloud 

Notes

Acknowledgements

The authors thank Stephen Muggleton and Dianhuan Lin for their assistance in running Metagol on the staircase data.

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Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia

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