Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection

F. Auat Cheein*, G. Steiner, G. Perez Paina, R. Carelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)


Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.

Original languageEnglish
Pages (from-to)195-207
Number of pages13
JournalComputers and Electronics in Agriculture
Issue number2
Publication statusPublished - Sept 2011


  • Agricultural mapping
  • Mobile robot
  • SLAM

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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