Authors:
Adam Kalisz
1
;
Mingjun Sun
1
;
Jonas Gedschold
2
;
Tim Erich Wegner
2
;
Giovanni Del Galdo
3
;
2
and
Jörn Thielecke
1
Affiliations:
1
Department of Electrical, Electronic and Communication Engineering, Information Technology (LIKE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Am Wolfsmantel 33, Erlangen, Germany
;
2
Institute for Information Technology, TU Ilmenau, Ehrenbergstraße 29, Ilmenau, Germany
;
3
Fraunhofer Institute for Integrated Circuits (IIS), Am Vogelherd 90, Ilmenau, Germany
Keyword(s):
Smart Farming, Semantic Segmentation, Visual, Localization, SLAM.
Abstract:
In recent years, crop monitoring and plant phenotyping are becoming increasingly important tools to improve farming efficiency and crop quality. In the field of smart farming, the combination of high-precision cameras and Visual Simultaneous Localization And Mapping (SLAM) algorithms can automate the entire process from planting to picking. In this work, we systematically analyze errors on trajectory accuracy of a watermelon field created in a virtual environment for the application of smart farming, and discuss the quality of the 3D mapping effects from an optical point of view. By using an ad-hoc synthetic data set we discuss and compare the influencing factors with respect to performance and drawbacks of current state-of-the-art system architectures. We summarize the contributions of our work as follows: (1) We extend ORB-SLAM2 with Semantic Input which we name SI-VSLAM in the following. (2) We evaluate the proposed system using real and synthetic data sets with modelled sensor no
n-idealities. (3) We provide an extensive analysis of the error behaviours on a virtual watermelon field which can be both static and dynamic as an example for a real use case of the system.
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