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Object Detection and Terrain Classification in Agricultural Fields Using 3D Lidar Data

  • Mikkel Kragh
  • Rasmus N. Jørgensen
  • Henrik Pedersen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9163)

Abstract

Autonomous navigation and operation of agricultural vehicles is a challenging task due to the rather unstructured environment. An uneven terrain consisting of ground and vegetation combined with the risk of non-traversable obstacles necessitates a strong focus on safety and reliability. This paper presents an object detection and terrain classification approach for classifying individual points from 3D point clouds acquired using single multi-beam lidar scans. Using a support vector machine (SVM) classifier, individual 3D points are categorized as either ground, vegetation, or object based on features extracted from local neighborhoods. Experiments performed at a local working farm show that the proposed method has a combined classification accuracy of \(91.6\,\%\), detecting points belonging to objects such as humans, animals, cars, and buildings with \(81.1\,\%\) accuracy, while classifying vegetation with an accuracy of \(97.5\,\%\).

Keywords

Object detection Terrain classification Agriculture Lidar 

Notes

Acknowledgements

This research is sponsored by the Innovation Fund Denmark as part of the project “SAFE - Safer Autonomous Farming Equipment” (project no. 16-2014-0).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mikkel Kragh
    • 1
  • Rasmus N. Jørgensen
    • 1
  • Henrik Pedersen
    • 1
  1. 1.Department of EngineeringAarhus UniversityAarhusDenmark

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