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Analysis of two visual odometry systems for use in an agricultural field environment
University of Skövde, Skövde, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2018 (English)In: Biosystems Engineering, ISSN 1537-5110, E-ISSN 1537-5129, Vol. 166, p. 116-125Article in journal (Refereed) Published
Abstract [en]

This paper analyses two visual odometry systems for use in an agricultural field environment. The impact of various design parameters and camera setups are evaluated in a simulation environment. Four real field experiments were conducted using a mobile robot operating in an agricultural field. The robot was controlled to travel in a regular back-and-forth pattern with headland turns. The experimental runs were 1.8–3.1 km long and consisted of 32–63,000 frames. The results indicate that a camera angle of 75° gives the best results with the least error. An increased camera resolution only improves the result slightly. The algorithm must be able to reduce error accumulation by adapting the frame rate to minimise error. The results also illustrate the difficulties of estimating roll and pitch using a downward-facing camera. The best results for full 6-DOF position estimation were obtained on a 1.8-km run using 6680 frames captured from the forward-facing cameras. The translation error (x, y, z) is 3.76% and the rotational error (i.e., roll, pitch, and yaw) is 0.0482 deg m−1. The main contributions of this paper are an analysis of design option impacts on visual odometry results and a comparison of two state-of-the-art visual odometry algorithms, applied to agricultural field data. © 2017 IAgrE

Place, publisher, year, edition, pages
London: Academic Press, 2018. Vol. 166, p. 116-125
Keyword [en]
Visual odometry, Agricultural field robots, Visual navigation
National Category
Signal Processing Robotics
Identifiers
URN: urn:nbn:se:hh:diva-35853DOI: 10.1016/j.biosystemseng.2017.11.009Scopus ID: 2-s2.0-85037985130OAI: oai:DiVA.org:hh-35853DiVA, id: diva2:1166228
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2018-01-09Bibliographically approved

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